Development of a Limit State Design Methodology for Railway … · 2010. 6. 9. · revision of the ‘Permanent way materials: prestressed concrete sleepers’ code (AS1085.14, 2003)

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Development of a

Limit State Design Methodology

for

Railway Track

by Jeffrey Leong

BE (Civil)

A Thesis Submitted for the Degree of Master of Engineering

School of Civil Engineering

Queensland University of Technology

November 2007

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Abstract

The research presented in this thesis is aimed at developing a limit state design

methodology for railway track for recommendation to Standards Australia’s next

revision of the ‘Permanent way materials: prestressed concrete sleepers’ code

(AS1085.14, 2003).

There is widespread suspicion that the railway track, particularly concrete sleepers,

have untapped reserves of strength that has potential engineering and economic

advantages for track owners. Through quantifying the effects of train speed, wheel

impact loadings and distribution of vehicle loads, track engineers would be able to

design railway track more accurately and hence uncover the reserves of strengths in

railway track.

To achieve this improvement a comprehensive set of wheel/rail impact

measurements has been collected over a one year period to establish a distribution of

track loadings. The wheel/rail impact data collected showed a logarithmically linear

distribution which shows that impact forces are randomly occurring events. The

linearity of the data also allows for wheel/rail impact forces to be forecasted allowing

for a more rational risk based design of the railway track.

To help with an investigation of the influence of changes to train operation on the

wheel/rail impact force distributions, development of a new dynamic track computer

model capable of simulating the complex interaction between the train and track was

completed within this research. The model known as DTRACK (Dynamic analysis

of rail TRACK) was benchmarked against other dynamic models and field data to

validate its outputs.

The field measurements and DTRACK simulations became the basis for

development of a limit state design methodology for railway track (risk based

approach) for railway track in place of an allowable limit state (compliance based)

approach. This new approach will allow track owners to assess the track capacity

based on more realistic loads and is expected to allow an increase in the capacity of

existing track infrastructure which will allow railways to be more commercially

competitive and viable.

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Table of Contents

Abstract ....................................................................................................................... iii

Table of Contents ........................................................................................................ iv

List of Figures ............................................................................................................. ix

List of Tables ............................................................................................................ xvi

Notation.................................................................................................................... xvii

Acronyms ................................................................................................................. xvii

Computer Model Names .......................................................................................... xvii

Statement of Originality.......................................................................................... xviii

Acknowledgements ................................................................................................... xix

Chapter 1 Introduction

1.1 Background of the Research........................................................................ 1

1.2 Rail CRC Project Aim ................................................................................. 2

1.3 Scope of this Research................................................................................. 3

1.4 Methodology................................................................................................ 3

1.5 Structure of this Research............................................................................ 4

Chapter 2 Railway Track Terminology, Design & Standards

2.1 Introduction.................................................................................................. 6

2.2 Railway Terminology .................................................................................. 7

2.2.1 The Vehicle .......................................................................................... 7

2.2.2 Wheel/rail Interface............................................................................ 10

2.2.3 The Track Structure............................................................................ 13

2.3 Contemporary Railway Track Design........................................................ 15

2.4 Australian Standards for Railway Track .................................................... 18

2.5 International Standards for Railway Track ................................................ 20

2.6 Other Standards for Railway Track............................................................ 23

2.7 Summary .................................................................................................... 27

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Chapter 3 Dynamic Track Simulation Model - DTRACK

3.1 Introduction................................................................................................ 28

3.2 Modifications and Upgrades to DTRACK................................................. 29

3.2.1 Modifications and Upgrades to DTRACK Codes.............................. 29

3.2.2 Modifications and Upgrades to DTRACK User Friendly Interface .. 31

3.3 Using DTRACK......................................................................................... 32

3.3.1 DTRACK Interface Layout................................................................ 32

3.3.2 Undertaking an Investigation ............................................................. 35

3.4 Summary .................................................................................................... 53

Chapter 4 Benchmark II

4.1 Introduction................................................................................................ 54

4.2 Benchmark II Input Parameters and Instructions....................................... 57

4.2.1 Requested Simulations ....................................................................... 58

4.2.2 Vehicle Parameters............................................................................. 59

4.2.3 Lara Test Site ..................................................................................... 60

4.2.4 Track Parameters................................................................................ 61

4.2.5 Wheel/Rail Properties ........................................................................ 63

4.2.6 Requested Simulation Outputs........................................................... 63

4.2.7 Vehicle Submodels ............................................................................ 64

4.2.8 Wheel/Rail Interface Submodels ....................................................... 64

4.2.9 Track Submodels................................................................................ 65

4.3 Benchmark II Results................................................................................. 66

4.3.1 Output Parameters.............................................................................. 66

4.3.2 Normal Contact Force Between Wheel/Rail...................................... 66

4.3.3 Shear Force in Rail at Midspan.......................................................... 69

4.3.4 Vertical Acceleration of the Rail at Midspan..................................... 71

4.3.5 Bending Moment at the Rail Seat of Sleeper ..................................... 73

4.3.6 Bending Moment at the Midspan of Sleeper ..................................... 75

4.4 Summary .................................................................................................... 77

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Chapter 5 Measurements of Wheel/Rail Forces

5.1 Introduction................................................................................................ 80

5.2 Wheel Condition Monitor (WCM) Systems .............................................. 81

5.2.1 Teknis Wheel Condition Monitoring System (WCM)....................... 81

5.2.2 Wheel Condition Monitoring Systems............................................... 81

5.2.3 Wheel Condition Monitoring Database (WCM Database) ................ 84

5.3 Processing of Data ..................................................................................... 86

5.4 Presentation and Interpretation of Data ..................................................... 88

5.4.1 Impact Force Distributions................................................................. 89

5.4.2 Effect of Speed on Impact Force Distributions.................................. 93

5.4.3 Axle Load Distributions................................................................... 100

5.5 Summary .................................................................................................. 102

Chapter 6 Time Analysis of Data

6.1 Introduction.............................................................................................. 104

6.2 Principles for Determining Design Load ................................................. 104

6.3 Establishing Probabilities for Impact Forces ........................................... 106

6.4 Consequences to Impact Forces Due to Varying Parameters .................. 110

6.4.1 Varying Velocities ........................................................................... 110

6.4.2 Varying Unsprung Mass .................................................................. 113

6.4.3 Varying Suspension Characteristics................................................. 115

6.4.4 Varying Wheel Maintenance Practices ............................................ 118

6.5 Consequences of Varying Parameters...................................................... 120

6.6 Summary .................................................................................................. 125

Chapter 7 Implications for Limit State Design of Railway Track

7.1 Introduction.............................................................................................. 127

7.2 Background on Limit State Design.......................................................... 128

7.2.1 Limit State Concepts ........................................................................ 128

7.2.2 Limit State Methodology ................................................................. 129

7.2.3 Material Resistance .......................................................................... 133

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7.2.4 Load Effects ..................................................................................... 134

7.3 Definition of a ‘Failed’ Concrete Sleeper and Limit State Conditions.... 136

7.4 Formulation for the Calculation of Design Wheel Load.......................... 139

7.5 Case Study................................................................................................ 149

7.6 Implications for Railway Businesses ....................................................... 152

7.7 Summary .................................................................................................. 154

Chapter 8 Implications for Limit State Design of Railway Track

8.1 Introduction.............................................................................................. 155

8.2 Findings and Conclusions ........................................................................ 156

8.3 Recommendations.................................................................................... 160

REFERENCES....................................................................................................... 162

APPENDICIS ......................................................................................................... 168

Appendix A Vehicle & Track Parameters included in Dynamic Impact Factor

Formulae (Tew et al, 1999) Appendix B Benchmark II instructions for Models of Railway Track Dynamic

Behaviour Appendix C Benchmark II results

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List of Figures Figure 2.1 Components of the vehicle (Kaiser & Popp, 2003)

Figure 2.2 Definitions of vehicle motions (Skerman, 2004)

Figure 2.3 Three piece bogie (Shabana and Sany, 2001)

Figure 2.4 (a) Wheel-rail contact (Knothe et al. 2001)

Figure 2.4 (b) Wheel-rail contact (Knothe et al. 2001)

Figure 2.5 Classical response to a wheel flat (Frederick, 1978)

Figure 2.6 General ballasted track configuration (Profillidis, 2000)

Figure 3.1 DTRACK’s error in modelling railpad force (Steffens, 2005)

Figure 3.2 Flow Chart for the Operation of DTRACK Interface (New

investigation) Adopted from Steffens (2005)

Figure 3.3 Flow Chart for the Operation of DTRACK Interface (open

investigation) Adopted from Steffens (2005)

Figure 3.4 DTRACK Desktop Icon

Figure 3.5 Menus available to user in DTRACK

Figure 3.6 Investigations Window in DTRACK

Figure 3.7 Track Tab

Figure 3.8 Track Diagram Accessed Via the ‘View Example Diagram’

Button

Figure 3.9 Rail Properties Window

Figure 3.10 Wheel or Rail Irregularity Tab

Figure 3.11 Example of a *.csv File for Arbitrary Rail Profile Input

Figure 3.12 Analysis Tab

Figure 3.13 Advance Setup Window

Figure 3.14 Vehicle Tab

Figure 3.15 Vehicle Properties Window

Figure 3.16 Comments Tab

Figure 3.17 Multiple Runs Tab

Figure 3.18 Run DTRACK option becomes available when data input is

completed

Figure 3.19 Results Setup Window

Figure 4.1 Lara Test Site, Melbourne to Geelong Track Line, Victoria

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Figure 4.2 Typical example of a cross section of railway track at Lara

Figure 4.3 (a) Profile 1 – To Melbourne (UP Direction)

Figure 4.3 (b) Profile 2 – To Geelong (DOWN Direction)

Figure 4.4 (a) Wheel/Rail Contact Force for Leading Wheel ‘Ideal’ Rail

Longitudinal Profile

Figure 4.4 (b) Wheel/Rail Contact Force for Leading Wheel for Arbitrary Rail


Figure 4.5 (a) Shear Force in Rail for ‘Ideal’ Rail Longitudinal Profile

Figure 4.5 (b) Shear Force in Rail for Arbitrary Rail Longitudinal Profile

Figure 4.6 (a) Vertical Acceleration of the Rail at Midspan before Sleeper C for

‘Ideal’ Longitudinal Rail Profile

Figure 4.6 (b) Vertical Acceleration of the Rail at Midspan before Sleeper C for

Arbitrary Longitudinal Rail Profile

Figure 4.7 (a) Sleeper Bending Moment at Rail Seat for ‘Ideal’ Longitudinal Rail

Profile

Figure 4.7 (b) Sleeper Bending Moment at Rail Seat for Arbitrary Longitudinal

Rail Profile

Figure 4.8 (a) Bending Moment at Sleeper Centre for ‘Ideal’ Rail Longitudinal

Profile

Figure 4.8 (b) Bending Moment at Sleeper Centre for Arbitrary Rail


Figure 5.1 (a) Teknis WCM Braeside Site

Figure 5.1 (b) Teknis WCM Raglan

Figure 5.2 Teknis WCM Hardware (Teknis, 2005)

Figure 5.3 Overview of the Teknis System (Teknis, 2005)

Figure 5.4 Example of Entries in Teknis WCM Database

Figure 5.5 Example of Processed Data from Excel

Figure 5.6 Impact Forces VS No. of Wheels (Empty)

Figure 5.7 Impact Forces VS No. of Wheels (Empty), Normalised

Figure 5.8 Impact Forces VS No. of Wheels (Full)

Figure 5.9 Impact Forces VS No. of Wheels (Full), Normalised

Figure 5.10 (a) Impact Force VS Speed - Braeside (Empty)

Figure 5.10 (b) Impact Force VS Speed - Raglan (Empty)

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Figure 5.11 (a) Impact Force VS Speed – Braeside Normalised (Empty)

Figure 5.11 (b) Impact Force VS Speed – Raglan Normalised (Empty)

Figure 5.12 (a) Impact Force VS Speed – Braeside (Full)

Figure 5.12 (b) Impact Force VS Speed – Raglan (Full)

Figure 5.13 (a) Impact Force VS Speed – Braeside Normalised (Full)

Figure 5.13 (b) Impact Force VS Speed – Raglan Normalised (Full)

Figure 5.13 (c) Impact Force VS Speed – Expanded View Braeside Normalised

(Full)

Figure 5.13 (d) Impact Force VS Speed – Expanded View Raglan Normalised

(Full)

Figure 5.14 (a) Number of Axles VS Axle Load – Braeside

Figure 5.14 (b) Number of Axles VS Axle Load – Raglan

Figure 6.1 (a) Braeside - Impact Forces VS Number of Axles (Full & Empty)

Figure 6.1 (b) Raglan - Impact Forces VS Number of Axles (Full & Empty)

Figure 6.2 (a) Empty Wagons – Narrow Gauge with Varying Speed

Figure 6.2 (b) Full Wagons – Narrow Gauge with Varying Speed

Figure 6.3 (a) Empty Wagons – Narrow Gauge with Varying Unsprung Mass

Figure 6.3 (b) Full Wagons – Narrow Gauge with Varying Unsprung Mass

Figure 6.4 (a) Empty Wagons – Narrow Gauge with Varying Damping

Figure 6.4 (b) Full Wagons – Narrow Gauge with Varying Damping

Figure 6.5 (a) Empty Wagons – Narrow Gauge with Varying Suspension

Stiffness

Figure 6.5 (b) Full Wagons – Narrow Gauge with Varying Suspension Stiffness

Figure 6.6 (a) Empty Wagons – Effect of Wheel Flat Sizes on Impact Force

Figure 6.6 (b) Full Wagons – Effect of Wheel Flat Sizes on Impact Force

Figure 6.7 (a) Impact Force Distributions due to the Effects of Train Speed at

Braeside

Figure 6.7 (b) Impact Force Distributions due to the Effects of Train Speed at

Raglan

Figure 6.8 (a) Impact Force Return Period Prediction (Braeside)

Figure 6.8 (b) Impact Force Return Period Prediction (Raglan)

Figure 6.9 (a) Impact Force VS Speed VS Wheel Flat Size (Empty)

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Figure 6.9 (b) Impact Force VS Speed VS Wheel Flat Size (Full)

Figure 7.1 Probability density functions of load and strengths (Campbell and

Allen, 1977)

Figure 7.2 Variations in probability functions with varying safety factors

(Wright, 2000)

Figure 7.3 (a) Impact Force Factor for Braeside

Figure 7.3 (b) Impact Force Factor for Raglan

Appendix C1 Simulation 1 RQTY Wagon 52t at 101.7km/hr Ideal

Longitudinal Rail Profile

Figure C1.1 Sim 1D – Vertical Acceleration at End of Sleeper C for ‘Ideal’


Figure C1.2 Sim 1E – Vertical Acceleration at Mid Span of Sleeper C for ‘Ideal’


Appendix C2 Simulation 2 RQTY Wagon 78t at 110.8km/hr Ideal Longitudinal Rail Profile

Figure C2.1 Sim 2A – Wheel/Rail Contact Force for Leading Wheel for ‘Ideal’

Longitudinal Rail

Figure C2.2 Sim 2B – Shear Force in Rail for ‘Ideal’ Longitudinal Rail

Figure C2.3 Sim 2C – Vertical Acceleration of the Rail at Midspan before Sleeper C

for ‘Ideal’ Longitudinal Rail





Figure C2.6 Sim 2F – Sleeper Bending Moment at Rail Seat for ‘Ideal’ Longitudinal

Rail Profile

Figure C2.7 Sim 2G – Sleeper Bending Moment at Centre for ‘Ideal’ Longitudinal

Rail Profile

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Appendix C3 Simulation 3 RKWF Wagon 28t at 75.0km/hr Ideal



Longitudinal Rail









Rail Profile


Rail Profile

Appendix C4 Simulation 4 RKWF Wagon 100t at 83.1km/hr Ideal



Longitudinal Rail









Rail Profile


Rail Profile

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Appendix C5 Simulation 5 RQTY Wagon 52t at 101.7km/hr Actual






Appendix C6 Simulation 6 RQTY Wagon 78t at 110.8km/hr Actual



Longitudinal Rail









Rail Profile


Rail Profile

Appendix C7 Simulation 7 RKWF Wagon 28t at 75.0km/hr Actual



Longitudinal Rail








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Rail Profile


Rail Profile

Appendix C8 Simulation 8 RKWF Wagon 100t at 83.1km/hr Actual



Longitudinal Rail









Rail Profile


Rail Profile

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List of Tables

Table 2.1 Maximum bending moment at the rail seat (AS1085.14, 2003)

Table 2.2 Table 2.2 - Maximum bending moment at the centre (AS1085.14,

2003)

Table 3.1 Types of Irregularity that can be simulated (Steffens, 2005)

Table 3.2 Explanation of the Multiple Runs Window Options in Figure 3.17

Table 4.1 Benchmark I Participants

Table 4.2 Benchmark II Participants

Table 4.3 Wagon Parameters

Table 4.4 Track Components

Table 4.5 Requested Output Parameters

Table 4.6 Theories of Mechanical Behaviour used in Models

Table 4.7 Output Parameters Presented

Table 4.8 Correlation between models and Lara field data

Table 4.9 Summary of Results

Table 7.1 Proposed Wheel Maintenance Factors

Table 7.2 Possible Track Importance Factor Values

Table 7.3 Proposed Track Importance factors

Table 7.4 Braeside Track Parameters

Table 7.5 Braeside Parameters

Table 7.6 Rail Seat Bending Moment M*

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Notation BOEF Beam on Elastic Foundation

DSM Discretely Supported Model

FEM Finite Element Model

DoF Degree of Freedom

Acronyms Rail CRC Cooperative Research Centre for Railway Engineering and Technologies

QR Queensland Rail

QUT Queensland University of Technology

UoW University of Wollongong

CQU Central Queensland University

CRE Centre for Railway Engineering

RC Rail Corporation (NSW)

ARTC Australian Rail Track Corporation

ARA Australasian Railways Association Inc.

ROA Railways of Australia

RSSB British, Rail Safety and Standards Board

RTRI Railway Technical Research Institute

DFG German Research Council

CHARMEC CHAlmers Railway MEChanics

AAR American Association of Railroads

TTCI Transportation Technology Center Incorporated

Computer Model Names DARTS Dynamic Analysis of Rail Track Structures

DIFF Vehicle-Track Dynamic Analysis Model

DTRACK Dynamic Analysis of Track

NUCARS™ New and Untried Car Analytic Regime Simulation

SUBTTI Subgrade Train-Track Interaction

VIA Vehicle Interacting with track Analysis model

VICT Interactions between Cars and Tracks

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Statement of Originality The work contained in this thesis has not been previously submitted for a degree or diploma at any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made Signed: ……………………………… Jeffrey Leong Date: ………………………………

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Acknowledgements The writer would like to thank Dr Martin Murray and Messrs John Powell, Nick Wheatley and Peter Hermann for their time, education and motivation in the completion of this research. The writer is also grateful to Queensland Rail, particularly Messrs Brian Hagaman and Ernie McCombe for the opportunity to undertake a Masters of Engineering by Research. The writer would also like to thank the Rail CRC steering committee for Project 5/23, including Messrs Karl Ikaunieks, Ric Lewtas, Steve Douglas, John Cowie and Sakdirat Kaewunruen Dr Alex Remennikov for their guidance and support. An acknowledgment also goes to Zhenqi Cai and Mr Clayton Firth for their dedication, contributions and efforts in developing the DTRACK model. The writer also wishes to recognize the contribution made by the participants of the Benchmark II Test undertaken as part of the research, including Mr David Steffens, Drs Alejandro Roda Buch, Anton Kok, Jens Nielsen, Ulf Gerstberger, Luis Baeza, Nick Wilson, Xinggao Shu and Professor Coenraad Esveld. Thanks also go to Mr Ian Telford for his brilliant knowledge, skills and assistance in spread sheeting that have helped the writer to complete his thesis. The writer also thanks the support of his family and friends who have given him their time and support during the course of his candidature.

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Accepted Abstracts Leong, J., Steffens, D.M. and Murray, M.H. (2006), Examination of Railway Track

Dynamic Models, International Heavy Haul Conference, 11-13 June, Kiruna,

Sweden.

Leong, J. and Murray, M.H. (2006), Probabilistic Analysis of Train/Track Impact

Forces, Journal of Engineering Mechanics, American Society of Civil Engineers.

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CHAPTER 1

Introduction

1.1 Background of the Research

Railway track owners in Australia are under increasing commercial pressures to

extract as much performance as possible from their track asset without wholesale or

catastrophic failure. To achieve this, track owners are increasing the operational

speeds and carrying capacities of the railway track. However, there is insufficient

knowledge of the dynamic loadings that the railway track is subjected to in its

lifetime and therefore the capacity of the track is not known. In addition, there is

widespread suspicion that the railway track, particularly concrete sleepers, have

untapped reserves of strength that have potential engineering and economic

advantages for track owners.

In 1996, the Australasian Railway Association Inc (ARA) initiated a review of the

Australian Standard ‘Permanent way materials: prestressed concrete sleepers’

(Standards Australia, 2003) to address the inadequacies in knowledge of track forces

and their transmission to and below concrete sleepers. The ARA prepared a brief

which noted the need for an approach that would clarify the railway loads and their

distribution into the track for application to the various types of railway operations in

Australia such as heavy haul, freight and passenger services.

Murray and Cai (1998) initiated a comprehensive literature review on research

related to concrete sleepers as a response to the ARA brief. The report found that a

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more cost effective appreciation of track performance could be realised with further

research, particularly with a more specific definition of the loading environment and

a better understanding of the flexural behaviour of the sleepers to impact loadings.

To address some of the issues identified by Murray and Cai (1998) a comprehensive

set of measurements of track forces of the various mix of traffic in Australia would

be needed to specify the definition of the loading environment. With a

comprehensive set of track forces data and the aid of a track analysis model, the

forces resulting from trains can be more accurately quantified and as a result railway

track owners will be able to make more efficient use of the track structure.

The need for measuring various dynamic traffic loading regimes (heavy haul, freight

and passenger) is that the risks associated with the various operations are all

different. For example, heavy haul and freight operations are based on commercial

risks whereas passenger traffic is based on safety risks. In addition, the dynamic

load profile is heavily dependent on the characteristics of the vehicle set up.

This research forms one of many research projects under the Cooperative Research

Centre for Railway Engineering and Technologies (Rail CRC). The project is titled

‘Dynamic Analysis of Track and the Assessment of its Capacity with Particular

Reference to Concrete Sleepers’. The project is a joint collaboration between the

Queensland University of Technology (QUT) and the University of Wollongong

(UoW) with the aim of developing in part a new limit state approach for the

Australian Standards AS1085.14 ‘Permanent Way Materials: Prestressed Concrete

Sleepers’ (Standards Australia, 2003).

1.2 Rail CRC Project Aim

The broad aim of this Rail CRC project is to help railway track owners to make more

cost effective use of the track asset through improved knowledge of track behaviour

under static, quasi static and dynamic loading and in particular through a more

realistic process of analysis for the design of concrete sleepers.

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The project aims to achieve the following:-

1. Complete the development of a software package for the rigorous analysis

of dynamic behaviour of railway track in Australia;

2. Establish a probabilistic based assessment methodology for railway track;

3. Develop a more realistic design approach for Standards Australia,

‘Prestressed Concrete Sleeper Code’ (AS1085.14, 2003); and

4. Provide track owners with saving flowing from increased confidence in the

capacity of track and sleepers to carry traffic.

The research presented in this thesis will also aim to provide the Australian railway

industry and research community with a limit state design methodology for railway

track loadings so that assessment of track capacity can be undertaken with

confidence.

1.3 Scope of this Research

The scope of this research includes:

1. Review of the current Australian Standards for railway track, limit state and

present track design procedures;

2. Development of a dynamic track computer model;

3. Collection and analysis of wheel/rail impact force data; and

4. Developing a limit state methodology for railway track.

1.4 Methodology

To extract further performance out of the track asset, a more realistic assessment of

the loading scenarios is needed to determine the boundaries of track capacity. The

research presented in this thesis aims to make the assessment of railway track based

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on more probabilistic loading scenario by establishing a limit state design

methodology for railway track.

To establish a limit state design methodology for railway track, the following would

be needed:-

1. A dynamic track model capable of simulating the track components reactions

to train loadings for future operations;

2. Validation of the model against actual track data;

3. Comprehensive set of wheel/rail impact data;

4. Establishment of probabilities and return periods for wheel impact events;

and

5. Develop a limit state methodology based on the collected data.

The outcomes of this research is to ultimately establish a limit state design

methodology for railway track that is capable of being adapted to the other types of

railway operations in Australia.

1.5 Structure of this Research

The structure of this research will be separated into the following parts:

1. Present a background on the definitions of a railway system, the current

design practices and standards for railway track;

2. Development and validation of a track dynamic computer model;

3. Measurement and analysis of wheel/rail forces; and

4. Development of a limit state design methodology for railway track.

Chapter 1 introduces the research and presents its purpose, methodology and

expected outcomes of the research.

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Chapter 2 provides background information on the railway system including

common terminologies and definitions used in railway engineering. The chapter also

presents the methodologies used in the design of railway track as well as the

standards that govern the design of track.

Chapter 3 presents the development and updates of a dynamic track model that will

be used for this research. The chapter also provides a case study as a guide on the

models use.

Chapter 4 presents a benchmarking exercise which compares field data against the

outputs of the various participating dynamic models against the dynamic model

developed in this thesis. A discussion on the merits and disadvantages of the

developed dynamic model is also provided in this chapter as well as justification on

its suitability for this research.

Chapter 5 provides a description on the equipment that was used to collect the

wheel/rail data and how the data was processed for analysis. The chapter also

presents the collected wheel/rail force data and provides an insight on how the data

can be used to establish probabilities and return periods of impact forces.

Chapter 6 establishes a methodology to predict probabilities and return periods of

impact forces from the wheel/rail data. The chapter also investigates the influence of

varying parameters (such as changes to operational speed) that may alter the impact

force distributions.

Chapter 7 develops a limit state methodology for the design of railway track based

on the data presented in Chapter 5 and 6. The chapter also explains the implications

for railway businesses due to the standards being transformed into a limit state

principle.

Chapter 8 concludes the thesis and provides recommendation for further research that

was presented throughout this thesis.

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CHAPTER 2

Railway Track Terminology, Design and Standards

2.1 Introduction

The design of railway is very complex due to the nature of the loadings from the

train to the railway track. Railways are traditionally separated into two systems, the

rollingstock and the railway track. This thesis will focus on the latter, however it is

very important to understand the train and track system as a whole as the two

systems are intertwined.

This chapter will present the common terminology used to discuss the railway

system and the current design methodologies used for track design. The standards

that govern the design of railway track in Australia will also be reviewed as it is

important to understand the limitations of the current standards and procedures.

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2.2 Railway Terminology

The vocabulary used to describe railway components varies between countries and

even railway organisations. This section will explain some of the most common

terminology used in Australia and within this thesis, to provide a general background

into railways.

The railway system is generally separated into three main parts:-

1. The vehicle;

2. The wheel/rail interface; and

3. The track structure.

2.2.1 The Vehicle

Rollingstock is the typical term used to describe trains and is composed of two types

of vehicles that enable the train to operate, the locomotive or power car and the

wagons. The wagon is typically made up of a car body and two bogies as shown in

Figure 2.1.

Figure 2.1 Components of the vehicle (Popp and Schiehlen, 2003)

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This figure is not available online. Please consult the hardcopy thesis available from the QUT Library

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The car body is a container that carries the goods (human or material) of the train.

The car body has generally six motions of movement as shown in Figure 2.2 below.

Figure 2.2 Definitions of vehicle motions (Skerman, 2004)

The bogie is positioned under the car body and is responsible for guiding the train on

the rails. The most common type of bogie is the three piece bogie and as the name

suggests, is typically made up of three main parts; the wheelsets, sideframe and

bolster as seen in Figure 2.3.

Figure 2.3 Three piece bogie (Shabana and Sany, 2001)

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Three piece bogies provide poor ride quality and low levels of lateral stability due to

the bogie having only secondary suspension. The Secondary Suspension group is

located between the bolster and the side frame. It should be noted that wagons with

only secondary suspension generate higher impact loadings compared with wagons

with both primary and secondary suspension due to the higher unsprung mass (Sun,

2003).

Some wagons, notably passenger trains have additional suspension know as the

primary suspension group which is located between the wheel set and the side

frames. Primary suspensions provide significant improvements to lateral stability

and ride quality, but are more expensive to maintain than bogies with only secondary

suspension. For this reason, this type of set up is mainly found on passenger wagons.

The unsprung mass of a vehicle is the mass of the components which are not

dynamically isolated from the track by suspension elements. For example, the

unsprung mass of the bogie in Figure 2.3 would consist of the wheelset and

sideframe only.

The Bolster spans between the two side frames each end resting on Secondary

Suspension, which provide vertical and some lateral flexibility. A top centre casting

on the vehicle body rests on a recessed centre plate (centre bowl) in the bolster, its

rim preventing longitudinal or lateral relative movement.

Side Frames sit directly on top of the axle boxes or package bearing adaptors and tie

the two wheelsets together longitudinally, transferring the load from the wagon to the

wheelset. The wheelset is the assembly consisting of two wheels and bearings on an

axle. Two wheelsets are fitted to bogies at each end of the vehicle, which can yaw in

order to negotiate curves.

The Wheel is the contact element connecting the vehicle to the track. Wheels are

conical rather than cylindrical in shape. This promotes a centring effect that helps the

wheel set through curves and slight lateral displacements of the track (Esveld, 2001).

The wheel also has flanges on the inside of the track to prevent derailments.

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2.2.2 Wheel/rail Interface

The connection of the vehicle and track through the wheel-rail interface is critical for

the successful operation of trains. If the connection is interrupted through breakdown

of either system, a derailment could occur which may have significant consequences.

Figure 2.4 (a) & (b) shows how the entire train load is distributed down into the track

system through a very small contact area on each wheel.

Figure 2.4 (a) & (b) Wheel-rail contact (Knothe et al., 2001)

The Hertz theory (1887) theorises the stresses that occur at the wheel rail interface

in the vertical plane: the elastic deformation of the steel of the wheel and of the rail

creates an elliptic contact area. The dimensions of the contact ellipse are determined

by the normal force on the contact area and the hardness of the wheel and rail

running surfaces, while the ratio of the ellipse axes depends on the curvatures of the

wheel and rail profiles. The shape of the contact ellipse changes in relation to the

location of the wheel-rail contact area across the railhead. Inside the contact area, a

pressure distribution develops which in a cross section is shaped in the form of a

semi-ellipse with the highest contact pressure occurring at the centre (Esveld, 2001;

Knothe et al., 2001).

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Defects at the wheel/rail interface (such as wheel defects and dipped joints) can

cause dynamic impact forces to occur and induce significant forces into the railway

track. This thesis will only be examining the effects of wheel defects due to the

scope of the research and time constraints. In particular flat spots on the wheel tread

can occur at random with a high probability; impact forces caused by wheel flats are

not localised effects (such as the effects of a dipped joint) and can impact randomly

along a section of railway track.

The dynamic impact loads induced into the track by rollingstock are almost entirely

due to irregularities in the roundness of the wheels (Frederick, 1978). When

designing prestressed concrete sleepers, it is important to consider the magnitude of

the forces generated by wheel irregularities, particularly wheel flats, and the

probabilities of the event occurring.

Wheel flats are defined as a chord forming on the circumference of the wheel or

simply, a flat zone on the wheel circumference. Irregularities on the tread of the

wheel generate very high dynamic forces and are the most common peak forces

encountered by the track structure in its service life. Wheel irregularities are

typically classified into three categories: out of roundness wheels, tread damage from

loss of materials, flat zones (wheel flats) on the circumference. Wheel flats are

produced by the wheels locking during braking, moving off with the brakes on and

shunting a vehicle with brakes on (Tunna, 1988). Figure 2.5 shows a classical

response of the track to a wheel flat strike.

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Figure 2.5 Classical response to a wheel flat (Frederick, 1978)

The graph shows that as the wheel flat pivots on its leading edge, there is a period of

unloading. As the wheel/rail force drops to zero during this period of unloading, the

rail begins to rebound from its deflected shape and moves back towards the wheel,

thus attempting to separate itself from the sleeper and the ballast. A peak force is

then created (P1) due to the wheel/rail contact upon landing. Very soon after the

initial contact between the wheel/rail, a second peak is created due to the combined

wheel/rail masses impacting on the sleeper known as the P11/2 force. The third peak

(P2) is a result of the wheel/rail and sleeper masses colliding with the ballast

(Frederick, 1978).

Research undertaken by (Tunna, 1988) at British Rail defines three distinct

frequencies arising from these forces in response to wheel flat strike as:

- P1 – The wheel bouncing on the rail typically ~ 1500Hz

- P11/2 – The wheel and rail bouncing on the sleeper ~ 200Hz

- P2 – The wheel, rail and sleeper bouncing on the ballast ~ 45Hz

The effects of a freshly slid wheel flat can generate forces significant enough to

crack a concrete sleeper. However, it should be noted that these forces are not

continuously sustained. As the wheel flat eventually becomes rounded, it produces

lower frequency responses and which may reduce in magnitude at higher speeds.

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2.2.3 The Track Structure

The typical track structure used throughout Australia is ballasted track. Other types

of track systems such as slab track are also used, however this research will be

focusing on the ballasted track structure.

The components of ballasted track structures are grouped into two main categories:

- The superstructure consisting of the rails, rail pads, sleepers, ballast and sub

ballast (capping layer); and

- The substructure consisting of the subgrade (formation) and the insitu

material.

Figure 2.6 General ballasted track configuration (Profillidis, 2000)

Rails are the longitudinal steel members that directly guide the train wheels evenly

and continuously (Sun, 2003). Rails distribute the concentrated wheel loads to the

spaced sleeper supports. The rails are held to the sleepers by fasteners and resist

vertical, lateral, longitudinal and overturning moments of the rails.

Rail pads or plates are required between the rail seat and the sleeper surface

primarily as a damper to the dynamic loads induced into the track by rolling stock

and reduction of rail-sleeper contact attrition.

Sleepers are essentially elastic beams that span across and tie the two rails together.

They have several important functions including receiving the load from the rail and

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distributing it over the supporting ballast at an acceptable ballast pressure level,

holding the fastening system to maintain proper track gauge, and restraining the

lateral, longitudinal and vertical rail movement by anchorage of the superstructure

into the ballast. In addition, concrete sleepers provide a cant to the rails to help

develop proper rail-wheel contact by matching the inclination of the rail to the

conical wheel shape.

Ballast is the layer of crushed stone on which the sleepers rest. The ballast assists in

track stability by distributing and reducing load from the track uniformly over the

subgrade. It anchors the track in place against lateral, vertical and longitudinal

movement by way of irregular shaped ballast particles that interlock with each other.

Any moisture introduced into the system can easily drain through the ballast away

from the rails and sleepers. The coarse grained nature of ballast assists in track

maintenance operations due to its easy manipulation. The rough interlocking

particles also assist in absorbing shock from dynamic loads by having only a limited

spring-like action (Hay, 1982).

Sub ballast, also known as the capping layer is usually a broadly graded material

that assists in reducing the stress at the bottom of the ballast layer to a tolerable level

for the top of the subgrade. The sub ballast is usually an impervious material that can

prevent the inter penetration of the subgrade and ballast, thereby reducing migration

of fine material into the ballast which affects drainage. This layer also acts as a

surface to shed water away from the subgrade into drainage along the side of the

track.

Subgrade, also known the formation, is the soil that offers the final support to the

track structure. The subgrade bears and distributes the resultant load from the train

vehicle through the track structure. The subgrade facilitates drainage and provides a

smooth platform, at an established grade, on which the track structure rests.

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2.3 Contemporary Railway Track Design

The fundamental purpose of design is to produce a structure that performs

satisfactorily and is safe from collapse. Satisfactory performance implies that the

structure under all loads and possible load combinations has limited deformations

such that the function of the structure is not impaired (Hughes, 1980).

Design requires the determination of forces that are induced into the structure, then

designing the structure to resist these forces. However, in the design of railway

track, the forces induced into the track structure are complicated due to the nature of

loading from the vehicle traversing on the track. In addition the support condition of

the railway track structure is complex due to the numerous degrees of freedoms of

motion in the track structure.

When a railway vehicle traverses the track structure, it induces forces that are

different from static forces due to the general roughness and irregularity of the track

alignment. These forces are known as Quasi-Static Forces, which are dynamic

loads that are less than 10Hz (Zhang, 2000). Due to quasi-static forces having such

low frequencies, the track structure tends to react to these loads similarly to static

loadings.

The most common procedure for calculation of the quasi static force in Australia is

the methodology presented by the Railways of Australia (ROA) manual, A Review

of Track Design Procedures (Jeffs and Tew, 1991).

The present common method to calculate quasi-static forces in railway track is to

multiply the static design load by the Dynamic Impact Factor (Jeffs and Tew,

1992). The dynamic impact factor allows the quasi-static loads to be expressed as a

multiple for the appropriate static loads (Grassie, 1992). It should be noted that this

is an empirical method and ignores vertical track elasticity, which absorbs some of

the impact forces that are induced into the rail (Jeffs and Tew, 1992).

16

The determination of the dynamic vertical wheel load (PD) is expressed empirically

as a function of the static wheel load (PS) i.e.

SD PP φ=

Where DP = Design wheel Load

SP = Static wheel Load

φ = Dynamic impact factor (always 1≥ )

The determination of the dynamic impact factor varies from each rail organisation

and the various formulas are detailed in Appendix A. Standards Australia and the

ROA manual recommends the Modified Eisenmann Formula for the calculation of

the dynamic impact factor (Jeffs and Tew, 1991).

The Eisenmann formula is a statistical method proposed by Eisenmann (1972) and is

the most common method used for the calculation of the dynamic impact factor.

( ) sD PtP δη+= 1

Where δ = Track condition factor

η = Speed factor, where η=1 for v<60km/h and 140

601 −+=

vη for

v>60km/h

t = Upper confidence level (UCL) factor with values of:

t = 0 UCL = 50%

t = 1 UCL = 84.1%

t = 2 UCL = 97.7%

t = 3 UCL = 99.9%

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Research and field tests undertaken by Broadley et al. (1981) have suggested the

following track condition factors for Australian conditions:

δ = 0.1 For track in “very good” condition (TCI up to 35)

δ = 0.2 For track in “good” condition (TCI up to 45)

δ = 0.3 For track in “average” condition (TCI up to 55)

δ = 0.4 For track in “poor” condition (TCI up to 70)

δ = 0.5 For track in “very poor” condition (TCI over 70)

Where TCI means Track Condition Indicies

Broadley et al. (1981) also introduced a loading factor β to account for the difference

between empty and loaded vehicles. Therefore, the Modified Eisenmann Formula

becomes:

( ) sD PtP βδη+= 1

Where, β = 1 for loaded vehicles; and

2 for unloaded vehicles.

The use of an empirical methodology to calculate the forces induced into the track is

not uncommon. There are numerous different formulas used to calculate the

dynamic impact factor and the methodology selected is dependent on the railway

organisation. A comparison of the other various impact factor formulas is presented

in Appendix A. It is apparent that some of the formulas are too simplistic, relating

only to vehicle parameters (e.g. vehicle speed and wheel diameter) (Jeffs and Tew,

1991).

It should also be noted that the empirical methodology used for railway track design

does not account for the dynamic impact forces that are generated by the defects at

the wheel/rail interface. Therefore, a more realistic dynamic analysis methodology is

required to determine the probabilities, return periods and magnitudes of impact

forces induced into the track.

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2.4 Australian Standards for Railway Track

In Australia, Standards Australia ‘Railway Track Material, Part 14: Prestressed

Concrete Sleepers’ code (AS1085.14, 2003) is the governing standard for the design,

manufacture and testing of concrete sleepers. For the determination of the design

loads, the code presents the modified Eisenmann methodology for the calculation of

the quasi static force and provides typical values of 1.4 to 1.6 times the static wheel

load for the calculation of the quasi-static force.

To account for the wheel/rail interface irregularities, the code provides another

empirical methodology for the calculation of high frequency dynamic forces. The

code states that a minimum allowance of 150 percent of the static wheel load shall be

used. However, research undertaken by Wakui and Okuda (1999), Jenkins et al.

(1974), Frederick (1978) have proven that high frequency dynamic forces can be up

to seven times the static wheel load depending on the size of the defect and vehicle

speed.

The code states that the combined quasi-static and dynamic design load factor shall

not be less than 2.5 times the static wheel load. The code also allows the combined

quasi-static and dynamic load to be equivalent to 2.5 to 3.0 times the static wheel

load for balanced loads at speeds of 80km/h and 115 km/h respectively in the

absence of a detailed analysis (AS1085.14, 2003 Clause F4). The series does not

take into account that some trains are currently operating at speeds in excess of

115km/hr and the neglect of the standard address to such increase in speed has led to

the need to update the code to suit contemporary operating conditions and

environment.

The code also does not consider the effect the track support systems (such as the

ballast layer) have on the dynamic impact loads. The code relies on the purchaser to

approve the design of track components that are suitable for their operational

environment and conditions. The reliance on the purchaser has advantages such as

designing the concrete sleeper for their specific environment, however the

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disadvantage is that there is no uniformity in the design methodology used in

Australia.

The code sets out the requirements for the design, manufacture, testing and

installation of the prestressed concrete sleepers. The design of these sleepers has

traditionally been based on prestressed concrete design principles, which can be

found in the Concrete Structures Codes AS3600 (2001).

The ‘Concrete Sleeper’ code (AS1085.14, 2003) is based on an allowable stress

principle. In allowable stress design, the adequacy of a structure is checked by

calculating the elastic stresses in the element due to the maximum expected loads and

comparing them with allowable stresses (Allen, 1982). The table below shows the

current methodology for the calculation of allowable bending moments that are

specified by the current AS1085.14 (2003).

Table 2.1 - Maximum bending moment at the rail seat (AS1085.14, 2003)

Distance between rail centres (g)

Maximum positive bending moment at

rail seat (MR+)

Maximum negative bending moment at rail seat (MR-)

g>1.5m (standard and broad gauge)

MR+ = R(L-g)/8 MR- = 0.67MR+ or 14kN.m whichever is less

1.5m>g>1.0m (narrow gauge)

MR+ = R(L-g)/6.4 MR- = 0.67MR+ or 14kN.m whichever is less

Where R = Rail seat load (kN) L = Length of sleeper (m) g = Gauge (m)

Table 2.2 - Maximum bending moment at the centre (AS1085.14, 2003)

Distance between rail centres (g)

Maximum positive bending moment at

centre (MC+)

Maximum negative bending moment at centre (MC-)

g>1.5m (broad gauge)

MC+ = 0.05R(L-g) MR- = 0.5[Rg – (Wg(L-g)) – W(2g - L)2/8]

g>1.5m (standard gauge)

MC+ = 0.05R(L-g) MR- = R(2g – L)/4

1.5m>g>1.0m (narrow gauge)

MC+ = 0.05R(L-g) MR- shall not be less than 14kN.m

Where W = 4R/(3L – 2g)

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The limitations of an allowable stress design approach to designing prestressed

concrete sleepers is that it may lead to over-design of the concrete sleeper because

allowable stress assumes that there is no post steel yield capacity which is not true in

practice (Allen, 1982). Therefore, more steel maybe needed than necessary to keep

the stresses below the allowable limit.

Other limitations of an allowable stress design include (Allen, 1982):

• Probability of loads occurring;

• Level of reliability required of structural members; and

• Neglect of the material strength of concrete and steel and overlooking

requirements for serviceability such as cracking, deflection and vibration.

The limitations associated with allowable stress principle that have been identified by

Allen (1982), Ellingwood and Galamblos (1982) and Hughes (1980) have led to most

structural design codes in the Australian Standard series being transformed to limit

state principles, which will be further investigated in Chapter 7.

2.5 International Standards for Railway Track

The calculation of track forces around the world are similar to the method presented

in the ROA (Jeffs and Tew, 1991), however there are different methods for

calculating the dynamic impact factor. The methods used by each rail organisation

are diverse, though they do have common factors such as vehicle speed, varied

relationships of vehicle/track construction and maintenance. Jeffs and Tew (1991)

presents a comparison of dynamic impact factor (see Appendix A). The two most

common methods to calculate the dynamic impact factor are the AREA method and

ORE method.

The American Railway Engineering Association (AREMA, 1999) has developed a

simplistic formula in calculating the dynamic impact factor known as the AREA

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method. AREA recommends the following method for the estimation of the dynamic

impact factor for design purposes.

Dv21.51+=φ

Where D = Wheel diameter considered.

v = Vehicle velocity (miles/hr).

The drawback of the AREA method is that considerations for wheel/rail irregularities

are neglected as well as other factors that can affect track dynamics such as

maintenance regimes and track condition. The AREA method is simplistic and the

literature review undertaken by Murray and Cai (1998) has shown that the AREA

method is conservative compared to other methodologies in calculating the dynamic

impact factor.

The Office of Research and Experiments (ORE, 1965) of the International Union of

Railways has developed an impact factor with coefficients that are based entirely on

measured track results of locomotives (Jeffs and Tew, 1991). The ORE impact

factor is determined by dimensionless coefficients.

'''1 γβαφ +++=

Where α’ = coefficient that is dependent on vehicle speed, vehicle suspension and

vertical track irregularities.

β’ = coefficient that is dependent on vehicle speed, superelevation

irregularities and location of the centre of gravity of vehicle

γ’ = coefficient that is dependent on vehicle speed, track condition, vehicle

design and maintenance conditions of locomotives.

The methods in calculating the three ORE coefficients vary between rail

organisations and are dependent on the many factors that can affect vehicle dynamics

(Jeffs and Tew, 1991).

22

Similar to the empirical design methodologies presented by Jeffs and Tew (1991),

the empirical approaches used internationally are only representations of the quasi

static force induced into the track structure. These approaches do not cover impact

forces that can create failure in the concrete sleeper, therefore a different approach is

required to accommodate the impact forces that the track will encounter during

service life.

The European Standard, prEN13230-1: Railway Applications – Track – Concrete

Sleepers and Bearers (2002), is vague in its standards of design forces for prestressed

concrete sleepers and relies mainly on the purchaser. For example, clause 4.2.1 in

prEN13230-1 (2002) states that the design load is calculated by applying a dynamic

coefficient to the static wheel load. The dynamic coefficient takes into account the

normal dynamic effects of the wheel and track irregularities. The design load value

is the responsibility of the purchaser.

The advantage of the European Standard in adopting this stance is that the purchaser

is solely responsible for the design of the track structure and should be familiar and

experienced with the operational environment that the sleeper will be designed for.

Another advantage is that different countries that are within the European Union are

able to continue using existing infrastructure and develop standards to suit their

individual operational environments. However a disadvantage of the European

Standard is that it offers no guidance or limiting conditions that must be complied

with.

North American railway track standards are based on the American Railway

Engineering and Maintenance of Way Association (AREMA) manual which provide

guidelines for recommended practice (AREMA, 1999). AREMA does not offer any

limiting factors or guidance for design and has always left the standards to be the

prerogative of the individual railways based on the nature and characteristics of their

plant and operations and the specific characteristics of the geographical region or

regions through which they operate.

There has been research undertaken in Japan that focuses on the shift away from the

conventional allowable stress design approach to a contemporary design method that

23

is based on limit state principles for prestressed concrete sleepers. Studies by Wakui

and Okuda (1999) have identified that the primary hindrance to evolving the design

methodologies used in Japan is the complex dynamic behaviour of the concrete

sleeper under impact loading of the wheels. However, the current Japanese

Prestressed Concrete Sleeper Code (JIS-E1201, 1997), is still based on allowable

stress principles.

Internationally, the standards are still based on either the allowable stress principles

or rely on the purchaser/operator to specify the dynamic forces induced in railway

track. Most standards are developed by individual rail organisations which base their

standards on previous experience and the nature of their operations and geographical

conditions. In Australia, railway track asset owners have developed standards in

addition to AS1085.14 (2003), for their individual operations and characteristics, as

described in the following section.

2.6 Other Standards for Railway Track

Railway Track Asset Owners in Australia such as Queensland Rail (QR), Rail Corp

(RC) and Australian Railway Track Corporation (ARTC) have developed standards

and specifications for their own individual rail operations. The respective standards

limit the dynamic impact forces (P2 forces) generated by wheel/rail irregularities by

specifying the allowable size of wheel/rail defects. For example, in the ARTC

Freight Vehicle Specific Interface Requirements standards (2002) in Clause 2.6 – the

P2 force shall not exceed the limits specified in Rolling Stock Units (RSU) 120,

which refers to a P2 force of 200kN.

Queensland Rail has set guidelines detailing the limits for rollingstock wheel defects,

known as the Wheel Defect Identification and Rectification (STD/0026/TEC, 2001),

which identifies and limits the size of wheel defects to control the dynamic impact

forces induced into the track. The QR standard specifies that wheel flats (refer to

chapter 5.2.1) of 50mm or multiple wheel flats of 40mm are the upper limits, above

which the wheels are considered defective.

24

Rail Corp (RC) and Australian Railway Track Corporation (ARTC) have unified

standards due to the similarities of their respective infrastructure and operations.

Similar to the standards set by QR and internationally, the RC and ARTC standards

series (TDS01, 2005) specify the maximum allowable defect sizes on both the rail

and wheel to minimise the P2 forces caused by these defects.

The standards that are set by railway organisations in Australia are maintenance

standards that are written to limit the magnitude of dynamic forces induced into the

track structure. The standards are not analytical methodologies for assessing the

magnitudes of the allowable forces induced into the track, but are there for the safety

management of the railway.

Due to the varied gauges in Australia and the many different types of operating

conditions, it became necessary to develop a national standard from an engineering

perspective to minimise the number of standards that railways must conform to.

With the introduction of third party operators and maintainers into the Australian rail

network, this necessity became more evident and rail asset owners began to develop

a Code of Practice for the Defined Interstate Rail Network.

In November 1999, the Australia Transport Council agreed to fund an Inter-

Governmental Agreement for Rail Uniformity. As a result of this agreement, the

Australian Rail Operations Unit (AROU) was established to develop and implement

a Code of Practice for the Defined Interstate Rail Network for standard gauge

railways linking the major cities in Australia. The Code of Practice (ARA, 2003)

was written in part to replace the Manual of Engineering Standards and Practices

produced by the former Railways of Australia (ROA) Committee (Jeffs and Tew,

1992).

The Australian Rail Operations Unit was later incorporated into the Australian

Railway Association (ARA) which further developed the code of practice.

Currently, the code consists of five volumes:

• Volume 1 – General Requirements and Interface Management

25

• Volume 2 – Glossary

• Volume 3 – Operations and Safe Working

• Volume 4 – Track, Civil and Electrical Infrastructure

• Volume 5 – Rollingstock

Of particular interest to the track engineer in the context of this research are Volume

4, Part 3 and Volume 5, Part 2. These parts set performance criteria for both

rollingstock and track components within the defined interstate network. These

performance criteria include guidelines on wheel and rail discontinuities such as

peaked and dipped welds and wheel flats and set force limits.

Volume 4 Part 3: Infrastructure guidelines (ARA, 2003) details the performance

criteria for various track components such as rail, sleepers and ballast. Of particular

interest are the guidelines for dipped and peaked welds. For peak or dipped new

welds, the code of practice specifies limits of 0.5mm over a 1m straight edge. For

existing track, weld limits for dips and peaks have been set to 2mm over a 1m

straight edge.

Volume 5 Part 2; Rollingstock common requirements (2002) details the performance

criteria for rollingstock design and sets limits for the forces that rolling stock may

apply to the track. The code specifies that the P2 force induced into the track shall

not exceed 230kN for freight vehicles and 295kN for locomotives. The calculation

of the P2 force uses Jenkins et al. (1974) formula for design purposes.

Volume 5, Part 2, Section 8: Rollingstock common requirements (2002), Skidded

wheels, details the limits for wheel flats. The code of practice categorises wheel flats

into five grades;

Grade 1 – A single flat with length less than 25mm.

No action required.

Grade 2 – Wheel flats between 25mm and 40mm long or multiple Grade 1

Skids.

26

Freight vehicles shall have wheels re-examined for defects. No

other action is required

No speed restriction required.

Grade 3 – Wheel flats between 40mm and 60mm long or multiple Grade 2

Skids.

Freight vehicles shall be Green Carded “for repair”.

A speed restriction of 80km/hr should be placed on any vehicle

with Grade 3 flats.

Grade 4 – Wheel flats between 60mm and 100mm long.

Wheels found with this class of defect at pre-trip inspection,

terminal, depot or repair facility shall not under any circumstances

be permitted to enter or remain in service.

If defect is discovered en-route or at a location without adequate

repair facilities, the vehicle may continue to its destination or

location with suitable repair facilities at a maximum speed of

25km/h.

Grade 5 – Wheel flats greater than 100mm long.

The vehicle shall not be moved until the tread surface defect is

adequately rectified or wheel set replaced.

These standards limit the magnitude of the P2 force induced into the track by

specifying the maximum allowable defect size at the wheel and rail interface.

However, a study undertaken by Dong et al. (1994) shows that the properties of the

rail pad can significantly affect the magnitude of the P2 force on the concrete sleeper.

The research proposed within this thesis is unique as this literature review has not

found any Australian or international railway design practices and standards that are

based on limit state principles. The standards in Australia are based on allowable

stress principles where maximum allowable limits are set to minimise the effect of

traffic over track.

The limitations of basing the standards on allowable stress principles may lead to

over design of the track materials and hence produce an uneconomical outcome in a

27

commercial environment. Therefore, a contemporary design methodology based on

limit state principles is needed to address these limitations.

2.7 Summary

The common terminology used to describe the railway system (the vehicle,

wheel/rail interface and track structure) was presented in this chapter as a

background to this thesis. This chapter also presented contemporary railway track

design methodologies in Australia and internationally and illustrated the shortfalls of

the current track design methods.

The standards that govern the design of railway track in Australia and internationally

were also reviewed in this chapter and found that the standards are still based on

allowable stress principles. Standards based on allowable stress principles are a

disadvantage to designers as the theory does not consider the ultimate strength of

materials, probabilities of loads occurring and the risks associated with failure, which

can lead to structures being over designed and hence be uneconomical. Therefore

there is a need to update the standards to one that is based on probability and risk,

hence the introduction of limit state design principles, which will be further

investigated in Chapter 7.

In addition to Australian standards, many railway organisations in Australia have

their own ‘in house’ standards which govern the maximum allowable size of defects

allowed on railway track. These standards are operational standards that are

designed to minimise the dynamic impact forces that are caused by defects at the

wheel/rail interface.

In the development of a limit state based standard a comprehensive set of wheel/rail

data is needed to enable an appropriated probabilistic methodology to be established.

In addition, a comprehensive set of wheel rail data will allow for the determination of

magnitudes of forces and load combinations so the track can be designed with a more

realistic and defensible design.

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CHAPTER 3

Dynamic Track Simulation Model - DTRACK

3.1 Introduction

This research is the second Rail CRC Project 5/23 Master of Engineering that

follows the research undertaken by Steffens (2005). Steffens (2005) thesis focused

on the identification and development of a model for railway track dynamic

behaviour that met the criteria set by the Rail CRC as well as demonstrating potential

for further development.

For these reasons, Steffens (2005) identified the Dynamic TRACK model

(DTRACK) developed by Cai (1992) as the best model for research and development

for the Rail CRC. It should be noted that Steffens (2005) referred to DTRACK as

DARTS as Steffens (2005) was unaware that the name DARTS was already being

used by another dynamic track model developed by Esveld Consulting Services.

This chapter will examine the current updated version of DTRACK, its capabilities

and its limitations as a computer dynamic model. A case study on how to use the

DTRACK program is also presented in this chapter as the updated user friendly

interface is different to the original interface developed by Steffens (2005).

29

3.2 Modifications and Upgrades to DTRACK

The original DTRACK model was developed by Cai for his PhD thesis (1992)

“Modelling of rail track dynamics and wheel/rail interaction”. Steffens (2005)

further developed the model by building a friendly user interface onto the DTRACK

program. Since Steffens (2005), there have been further upgrades to Cai’s (1992)

DTRACK program and to Steffens (2005) user friendly interface.

3.2.1 Modifications and Upgrades to DTRACK Codes

Steffens Masters thesis (2005) identified various problems in the original DTRACK

program. Since then, the original author of DTRACK has been contracted to correct

these problems. There were three specific issues with the original DTRACK

program that were identified by Steffens (2005).

The first issue with DTRACK was found in the modelling of the quasi-static forces

applied to the track in ‘ideal’ wheel/rail contact conditions. When compared to other

dynamic models during the Benchmark I exercise (Steffens, 2005), the DTRACK

model calculated wheel/rail forces significantly lower than the other dynamic

models, which as a consequence affected the model’s estimation of the magnitudes

of forces throughout the system.

The second issue with the DTRACK model was the way DTRACK handled the

stiffness and damping properties of the rail pad. DTRACK had built-in assumptions

such as fixed maximum values for stiffness and damping values for the rail pad to

save computing time which was not necessary with more modern computers.

Another issue with the original DTRACK program is the output produced for the

sleeper pad reactions for both the concrete and timber sleeper case. The problem

occurs when a train wheel passes directly over the rail pad, DTRACK showed the

magnitude of the reaction of the rail pad dropping significantly. This behaviour was

not found in the any of the other models that participated in the Benchmark I exercise

as Figure 3.1 shows.

Figure 3.1 DTRACK’s error in modelling railpad force (Steffens, 2005)

The last problem identified by Steffens (2005) was the calculation of the sleeper

centre bending moments. The issue related to how DTRACK models the sleeper

dimensions, which greatly affects the magnitudes of the bending moment.

Upon the recommendation presented by Steffens (2005), the original author of

DTRACK (Dr. Zhenqi Cai) was contracted to correct these issues. Since the

correction of these problems, a second benchmark has been completed by the writer

to assess the capabilities of the revised DTRACK outputs. The second benchmark

compared DTRACK against the results of field data as well as outputs of other track

dynamic models and is presented in the next chapter.

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31

3.2.2 Modifications and Upgrades to DTRACK User Friendly Interface

Steffens Masters thesis (2005) was primarily focused on developing a user friendly

interface for the DTRACK program. However, Steffens (2005) did not complete the

interface and a computer programmer in partnership with the writer completed the

user friendly interface which forms a small part of this thesis.

The new interface that was developed for the DTRACK varies significantly from the

original interface. However, it should be noted that the original structure of the input

parameters has not changed.

New features that were developed for the upgraded interface include;

• Ability to undertake multiple runs which allows the user to undertake

multiple investigations with varying parameters such as various speeds and

analysis positions;

• A library that contains the parameters used investigations, for example

properties of rail, rail pads and sleepers which can be recalled;

• The outputs of DTRACK can now be graphically displayed within the

program without having to be exported to Microsoft Excel; and

• The ability to graph the results of different investigations against each other

for easier comparisons.

Many of the upgrades to the DTRACK interface were concentrated on the menus and

graphical outputs of the model. It should be noted that the original structure of the

input and output parameters of the model had not been altered and only the user

friendly interface was changed.

32

3.3 Using DTRACK

This section examines in detail how to use the DTRACK model through the new user

friendly interface. A copy of the DTRACK program is included on a CD that is

attached to this thesis, to allow installation of DTRACK onto a computer with

Microsoft Windows XP.

3.3.1 DTRACK Interface Layout

The general layout of DTRACK is similar to Steffens’ (2005) original work. Figures

3.2 and 3.3 are reproduced from Steffens (2005) Masters Thesis and illustrate the

investigation process for using DTRACK. Figure 3.2 shows the process taken for a

new investigation, whilst the flow chart in Figure 3.3 shows the process taken for

opening an existing investigation in DTRACK.

It should be noted that the DTRACK program was intended to be used by track

engineers who possess a good understanding of the parameters that are required by

DTRACK.

33

Figure 3.2 Flow Chart for the Operation of DTRACK Interface (New investigation)

Adopted from Steffens (2005)

InvestigationMenu

DTRACK Workspace

ResultsMenu

New ... Open ...

Investigation 1

Save As…

Investigation 1 . inv

FREVIB. INF DAMP1. INF

NATVIB.OUT RESULTS . OUT

Has all Databeen collectedsuccessfully ?

Save As ...

Investigation 1 . inv

DTRACK.INP Investigation 1

Run DTRACK

Yes

AB

DTRACK Workspace

Track Data Irregularity Data Analysis Data Vehicle Data Commentary Multiple Runs

Investigation Menu

No

HelpMenu

Exit

34

Figure 3.3 Flow Chart for the Operation of DTRACK Interface (open investigation)

Adopted from Steffens (2005)

Investigation1.inv

Investigation 1

Keep Track Data ?

Save As ...

Investigation 2 .invOr

New File Name

RESULTS . OUT

DRACK Workspace

Has all Data been collected successfully ?

Save As…

Investigation2.inv Or

New File Name

Run DTRACK

FREVIB.INFDAMP1.INF

NATVIB.OUTRESULTS.OUT

DTRACK.INPInvestigation1

DTRACK.INPInvestigation2

Save As...

Investigation2.invOr

New File Name

Yes

No Yes

A

B B

Keep Track Data Checkbox

No

Investigation1:FREVIB . INFDAMP 1 . INF

NATVIB . OUTInvestigation

Menu

Track Data Irregularity Data Analysis Data Vehicle Data Commentary Multiple Runs

35

3.3.2 Undertaking an Investigation

The DTRACK program is executable through Microsoft Windows XP from a

shortcut that the installation automatically places on the desktop or from the start

menu as shown in Figure 3.4.

Figure 3.4 DTRACK Desktop Icon

When the user first enters the program, there are three menus available to the user for

selection which include (as seen in Figure 3.5):-

• Investigation menu – for creating, opening and saving investigations;

• Results menu – for examining results from simulations; and

• Help menu – for further guidance on the interface.

Figure 3.5 Menus available to user in DTRACK

36

To start an investigation, the user enters the Investigation menu where two options

are available for selection:-

1. New – Creates a new investigation where parameters have to be specified.

2. Open – Loads up an existing investigation where parameters were previously

set.

For the purposes of the following example, the ‘New’ option will be selected where

an ‘Investigation’ window will pop up allowing the user to input parameters into the

program. Figure 3.6 shows the investigation window.

Figure 3.6 Investigations Window in DTRACK

37

When the ‘Investigation’ is opened, there are six tabs at the top of the window where

the input data has to be inserted into the boxes provided. The six tabs in Figure 3.6

are input parameters for:-

• Track – rail size, gauge, rail pad type, sleeper type, sleeper spacing and track

bed type;

• Irregularity – irregularity of rail (such as dipped joints) and wheel irregularity

parameters (such as wheel flat);

• Analysis – where position along the sleeper and rail where analysis is to be

performed;

• Vehicle – vehicle speed, vehicle type, bogie type, wheel radius;

• Comments – additional information related to the investigation; and

• Multiple Runs – Allows for multiple investigations to be undertaken without

having to change all the input parameters.

The Tabs in the ‘Investigation’ window are arranged so that the parameters are

entered progressively from track structure to vehicle.

The ‘Graph’ and ‘Run TRACK’ buttons in the ‘Investigation’ window are inoperable

until all the parameters have been entered into DTRACK by the user, therefore

cannot run the model with incomplete inputs. The functions of these two buttons

will be explained further on the in this chapter.

38

Track Tab

The Track Tab sets the parameters for the so called ‘below rail’ components. Figure

3.7 shows the inputs required under this tab.

Figure 3.7 Track Tab

Inputs under the Track Tab are ordered down the screen so that track structure can be

designed from the top down. The user may use the predefined component

parameters provided from the drop down boxes or select the ‘Properties’ button on

the right of the drop down boxes to specify component parameters that are not

available in the drop down boxes.

It should be noted that some options in the drop down boxes have been permanently

installed in the ‘library’ of DTRACK. Parameters such as Australian Standards rail

sections are standard parameters which cannot be edited or deleted. The user may

wish to add or edit other parameters in the ‘Library’ which can be done through the

‘Properties’ buttons on the right of drop down boxes. When the parameters have

39

been saved in the ‘Library’ the details automatically become available for later

investigations.

Only the ‘Properties’ button for rail is presented as the other ‘Properties’ buttons

operate similarly and are fairly self explanatory.

For the case study, a standard gauge concrete sleepered track with medium track bed

stiffness has been set up for the investigation.

The ‘View Example Diagram’ button in Figure 3.7 loads up a diagram of a track

structure as shown in Figure 3.8. The diagram of the track structure is intended to

assist the user to understand the terminology of the components of the track.

Figure 3.8 Track Diagram Accessed Via the ‘View Example Diagram’ Button

The ‘Keep Track Data’ at the bottom left hand corner of the window in Figure 3.7 is

to allow the user to preserve the track data for later investigations. The track data

will also be saved if the user selects the ‘Save’ option in the Investigation menu.

40

Rail Properties Window

The Rail Properties Window shown in Figure 3.9 can be accessed through the

properties button on the right hand side of the rail type box near the top of Figure 3.7.

The default rail sections in this window are Australian Standards rail sections which

cannot be changed.

The user may input a non Australian Standard rail section simply by clicking the

‘Add’ button in Figure 3.9, and then specifying all the properties of the rail. The

‘Apply’ button in Figure 3.9 will only appear once all the parameters have been

input, clicking this button will also preserve the new values the user has entered.

Figure 3.9 Rail Properties Window

41

Irregularity Tab

The Irregularity Tab in Figure 3.6 allows the user to define variations in the contact

conditions at the wheel/rail interface. Upon selection of this Tab, the user can

choose from the options as shown in Figure 3.10; no irregularity assumes that there is

perfect wheel/rail interaction; the other two choices compromise irregularities at the

rail or wheel and are limited to the choices in Table 3.1 below.

Table 3.1 Types of Irregularity that can be simulated (Steffens, 2005)

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This table is not available online. Please consult the hardcopy thesis available from the QUT Library

42

Table 3.1 can also be viewed in DTRACK by clicking onto the ‘View Example

Diagram’ button located on the lower left hand corner in Figure 3.10.

The characteristics of these wheel or rail defects can be selected using drop down

menu boxes; the length and depth of irregularity must be specified (see Figure 3.10).

The ‘No Irregularity’ option in Figure 3.10 creates a ‘perfect’ wheel/rail contact

condition in DTRACK (i.e. perfectly round wheels and perfectly flat rail and track).

Figure 3.10 Wheel or Rail Irregularity Tab

For the case study described later, an arbitrary rail profile is used for the

investigation (refer to Chapter 4.2.4 for more details), as illustrated in Figure 3.10.

The ‘Load CSV’ button in Figure 3.10 enables the user to input a previously created

comma delimited file (*.csv) of x and y coordinates of the longitudinal rail head

profile.

43

A *.csv file of an arbitrary rail profile must have three columns in the file, with the

first column being the number of the row of data and the remaining columns are x

and y coordinates. Figure 3.11 shows the Benchmark II Arbitrary Profile 2.csv file

that is used for this case study investigation.

Figure 3.11 Example of a *.csv File for Arbitrary Rail Profile Input

A significant number of data points can be used in the arbitrary profile file. However

it should be noted that the more data points used, the longer it will take DTRACK to

process. For this case study, 548 data points were used which represented

approximately 11.1m of track.

The same process can be used to load an arbitrary wheel profile in DTRACK and the

*.csv file must be in the same format as described above.

44

Analysis Tab

The Analysis Tab in Figure 3.6 allows the user to specify the position along the rail

or sleeper at which specific output data is required (for example at the rail seat or

sleeper centre), known as the ‘analysis position’. Figure 3.12 shows the layout of the

‘Analysis’ tab. The ‘View Example Diagram’ button in Figure 3.12 is there to assist

the user to determine the analysis locations.

Figure 3.12 Analysis Tab

The drop down boxes beside the ‘Rail Analysis Position’ and the ‘Sleeper Analysis

Position’ contain predefined locations along the rail and sleeper at which DTRACK

automatically calculates output results of moments, shear etc. For each drop down

box option DTRACK calculates the coordinate of the analysis point. For the case

study, the sleeper spacing is 680mm and therefore, for the ‘MidspanBeforeSleeper’

option, DTRACK calculates the y-coordinate along the rail as -340mm as shown in

Figure 3.12. The ‘Advance Setup’ button on the lower left hand corner will be

explained in the following section.

45

Advance Setup Window

The Advance Setup button at the lower right of Figure 3.12 takes the user to a

window which allows the user to define how DTRACK is to execute the analysis.

Figure 3.13 shows the parameters that can be changed by the user in the Advance

Setup window.

Figure 3.13 Advance Setup Window

The number of sleepers and sleeper number to be analysed are fixed so that boundary

effects of the model can be minimised. The default values in this menu were based

on the recommendations of the original DTRACK author and any changes to these

parameters should only be made by an informed user.

The Time Step Analysis Setup at the bottom of Figure 3.13 group can be defined by

the user. The time step for analysis and the time step write number defines which

analysis results will be reported in the DTRACK output files. If the user has entered

the wrong values, the Default button at the bottom left hand corner of the window

can be clicked to return the values in the boxes back to the default values.

46

Vehicle Tab

The Vehicle Tab in Figure 3.6 is for entering the so called ‘above rail’ data. The

parameters for the case study exercise are featured in Figure 3.14.

Figure 3.14 Vehicle Tab

Drop down boxes are again available as shown in Figure 3.14 for choosing from the

library of stored vehicle parameters only. The user may also change the properties of

the vehicle by selecting the properties button on the right hand side of the screen.

The properties button will be explained in the next section.

The speed of the vehicle during simulation must be entered in the text box. The

Hertzian Contact Coefficient in Figure 3.14 is calculated automatically by DTRACK

based on the vehicle and rail parameters.

The ‘View Example Diagram’ button at the bottom left of Figure 3.14 again provides

a diagram to assist the user during the input of data.

47

Vehicle Properties Window

The Vehicle Properties window pops up if the user clicks the ‘Properties’ button to

the right of the ‘Vehicle Type’ in Figure 3.14. The window allows the user to input

vehicle properties. The bogie and wheel type can be specified by using the drop

down boxes provided as shown in Figure 3.15. The user may also add new vehicles

to the library by selecting the ‘Add’ button as shown in Figure 3.15.

Figure 3.15 Vehicle Properties Window

The Vehicle Properties window operates similarly to the Rail Properties window

where the user can either used the parameters already in the library or input their own

vehicle parameter data for later reference.

The properties of the bogie and wheel can also be changed by the user through the

selection of the Bogie and Wheel Properties buttons shown on the right hand side of

Figure 3.14. Similar to the vehicle window, the bogie and wheel properties buttons

enable the user to change dimensions such as wheel spacing, spring stiffness, side

frame and bolster masses of the vehicle.

48

Comments Tab

The Comments Tab in Figure 3.6 allows the user to add any additional information

that is relevant to the investigation. The text boxes provided as shown in Figure 3.16

are there as an information management tool to assist the user and do not have any

influence on the results of DTRACK.

Figure 3.16 Comments Tab

All the information entered into the text boxes provided is automatically saved to the

investigation and can be viewed when the relevant investigation is reloaded in

DTRACK.

49

Multiple Runs Tab

The Multiple Runs Tab in Figure 3.6 enables the user to undertake multiple runs with

varying parameters without going back to the beginning of the program to re-enter all

the data from the start. Figure 3.17 shows the options available to the user in the

window accessed via the Multiple Runs tab.

Figure 3.17 Multiple Runs Tab

The Multiple Runs Tab is set up so the user can undertake multiple investigations at

various speeds (e.g. speeds at 60, 80 and 100km/hr), at various analysis positions or

with various irregularities without the need to re-enter the train and track data

repeatedly. Table 3.2 explains what each option on the Multiple Runs window

means.

50

Table 3.2 Explanation of the Multiple Runs Window Options in Figure 3.17

Option Explanation

Single Run only DTRACK will not save the data once the run is completed unless specified by the user

Vehicle Speed The user may change the speed of the vehicle

Irregularity Length The user may change the length of the irregularity on either the wheel or rail

Irregularity Depth The user may change the depth of the irregularity on either the wheel or rail

Centre of Irregularity This changes the location of the irregularity on either the wheel or rail

Rail Analysis Position This changes the position along the rail where the analysis is to take place, for example: midspan between sleepers

Sleeper Analysis Position This changes the position along the sleeper where the analysis is to take place, for example: the midspan or rail seat of the sleeper

The ‘Keep Track Data’ checkbox in Figure 3.17 also becomes available to the user

once all the data has been entered. This check box allows the user to undertake a

quicker analysis by fixing the details of the track structure. This reduces the analysis

time it takes DTRACK to run when repetitive analysis of different speeds and

analysis positions using the same track structure are to be completed.

After the investigation has been saved, the ‘Run DTRACK model’ button becomes

available as seen in Figure 3.18. Note that the name ‘Investigation’ at the top left

hand of the window has now changed to the file name the user has chosen. For the

case study, the file name in the top left of the window in Figure 3.18 has now

changed to ‘BM2 Test’.

51

Figure 3.18 Run DTRACK option becomes available when data input is completed

The program takes a few minutes to run, depending on the speed of the vehicle and

the time step setup in the ‘Advance Setup’ window the user had specified. The lower

the speed, the more iterations DTRACK will have to complete and therefore the

longer it will take to run.

Once the program has completed the simulations, the ‘Graph’ button in Figure 3.18

becomes available for graphical viewing of the results of the investigation. Clicking

on the Graph button brings up the ‘Results Setup’ window where the data can be

studied in detail.

52

Results Setup Window

The Results Setup Window is for the user to choose which graphs to view that were

produced in the investigation. The graphs are presented in either time or distanced

based domains. Figure 3.19 shows the Results Setup Window.

Figure 3.19 Results Setup Window

The ‘Load’ investigation button on the lower left hand corner of Figure 3.19 enables

the user to load a previous completed DTRACK investigation for analysis. The load

investigation button operates the same way as opening a file.

Only checked items are graphed in the Results Setup window (as seen by the

message at the bottom of the screen). Up to three graphs can be presented at the

same time during the analysis allowing for comparisons between the different

analysis positions. To plot the graphs, the user must select one to three items

available and then select the ‘Show Graph’ button.

53

3.4 Summary

The DTRACK model presented in this chapter is the completed revised version of

the program that was initiated by Steffens (2005). The original author of the

program (Cai, 1992) was contracted to correct the problems with DTRACK that were

identified by Steffens (2005). In addition a programmer in partnership with the

writer completed the user friendly interface for DTRACK as part of this thesis.

Visual Basic .NET (2002) was again chosen as the programming environment to

allow for the further development to the user friendly interface. The general

Microsoft Windows interface layout structure was maintained so that the

environment was familiar to the user.

Although the general structure of the user friendly interface presented in Steffens

(2005) Masters Thesis had not changed, many of the original features have been

upgraded and improved. Improvements such as library maintenance, data

management and graphing abilities were included in the upgraded DTRACK.

A general guide and working example on undertaking a track investigation in

DTRACK was presented in this chapter. A CD of the DTRACK program is attached

to this thesis allowing for a full installation of DTRACK onto a computer with

Microsoft XP. At the time of writing of this thesis, a more detailed instructional

manual for DTRACK was being written by the author.

54

CHAPTER 4

Benchmark Tests for Models of Railway Track Dynamic Behaviour - Benchmark II

4.1 Introduction

There have been several benchmarking exercises in the past that compared the results

of various computer models of railway track. Most notable were the benchmarking

exercises of Grassie (1995), Knothe (1995) and Iwniki (1998). In each of these

benchmarking exercises, the participants were requested to provide results for

comparative analysis when provided with a set of rigorously stipulated parameters.

The overall aim of these benchmarking exercises was to validate and compare the

results of the various dynamic models against one another. However, the

benchmarking exercises did not correlate the dynamic model results against any field

data and therefore could not be an accurate indication of what occurs in practice.

The last independent benchmarking exercise was the Manchester Benchmark

undertaken by Iwniki (1998). Since that time there have been a number of more

sophisticated dynamic track models that have been developed allowing for better and

more in-depth analysis and improved representation of the railway track.

55

Steffens and Murray (2003) initiated another benchmarking exercise as a response to

the long period of time since the Manchester Benchmark which will be referred in

this thesis as Benchmark I. Benchmark I (Steffens and Murray, 2005) was very

similar to the Manchester Benchmark (Iwniki, 1998), where the same standard gauge

passenger vehicle (described in the Manchester Benchmark (Iwniki, 1998)) travelling

at 160km/hr with various wheel/rail contact defects was simulated.

The railway research organisations that participated in Benchmark I are listed in

Table 4.1.

Table 4.1 Benchmark I Participants

Model Name Research Organisations

DTRACK DynTrack Systems, USA.

DIFF CHARMEC, Sweden.

NUCARS™ Transportation Technology Center, Inc, USA.

SUBTTI Technical University of Berlin, Germany.

TRACK* Stuart Grassie Engineering Solutions, UK

VICT Southwest Jiaotong University, China * TRACK is also known as ‘Track Design v3.4’

Steffens and Murray (2005) found that the participating models had a wide range of

complexity in the theoretical basis, construction of the models and the inputs

required. In addition, Steffens and Murray (2005) also found that the outputs of

various models were dependent on the assumptions taken by the user. For example

some models required certain input parameters for their models which were not

provided in the benchmark parameters.

Benchmark I also revealed that a single set of simulations representing only one

vehicle and track scenario was insufficient to draw conclusions regarding the

behaviour of railway track as the results were not compared to field data; a further

benchmarking exercise was therefore recommended.

56

The Benchmark II exercise was therefore initiated by Steffens to compare the outputs

from the dynamic track models against field data collected at Lara, which is situated

on the Melbourne - Geelong railway line and the exercise was completed by the

writer. Another objective of Benchmark II was to continue the forum of discussion

and information that was established in the Benchmark I exercise.

The primary aim of Benchmark II was to correlate the outputs of various railway

track dynamic models against each other and against the Lara field data.

Benchmark II also provided a good opportunity to test and compare the outputs

produced by the revised DTRACK against the other models and against field data to

examine whether the results were in general agreement with the other models and the

field data. The DTRACK results used for comparison in Benchmark II would also

justify the computer models suitability for the research in this thesis as well as

establishing confidence when DTRACK is eventually released commercially.

This section will examine the input parameters of Benchmark II, the equipment used

to collect the field data, the participant’s simulation outputs for Benchmark II and a

discussion of the correlation between the various track dynamic models and Lara

field data.

57

4.2 Benchmark II Input Parameters and Instructions

Many railway research organisations from around the world were again invited to

participate in the Benchmark II exercise. The participants of the Benchmark I

exercise were also invited to participate in Benchmark II. The participants that

accepted the invitation to participate in Benchmark II are shown in Table 4.2.

Table 4.2 Benchmark II Participants

Model Name Research Organisations

DARTS Delft University of Technology, Netherlands. DIFF CHARMEC, Sweden. DTRACK DynTrack Systems, Canada & USA. NUCARS™ Transportation Technology Center, Inc, USA. SUBTTI Technical University of Berlin, Germany. VIA University of Valencia, Spain.

The outputs of the DARTS program were supplied by Delft University of

Technology (Kok, 2005).

The DTRACK model was run on behalf of its author (Cai, 2005), using the updated

version, DTRACK v2.0.

The NUCARS™ results were based on a Beta version of NUCARS™ and were

provided by Transportation Technology Centre Incorporated (TTCI) (Wilson &

Xinggao, 2005).

The DIFF, SUBTTI and VIA benchmark results were produced by their respective

authors (Nielsen, 2005; Gerstberger, 2005; Buch, 2005).

The specifications were originally developed by Steffens (2004) and sent out to the

participants. Full details of the specifications can be found in Appendix B. The

following sections briefly explain the simulation parameters and requested results for

the Benchmark II exercise.

58

4.2.1 Requested Simulations

The Benchmark II exercise was based on two freight vehicles traversing on a

concrete sleepered track structure without any wheel/rail irregularities. The

requested simulations were to be based in two dimensions only.

The selection of the simulation freight vehicles was based on two factors. The first

factor was that most railway organisations around the world operated freight traffic

travelling on concrete sleepered ballasted track. The second factor was that

Benchmark I had already simulated a passenger vehicle and therefore a freight

vehicle will allow for more definitive comparisons between the benchmarked

models.

Although no irregularities at the wheel/rail interface (such as wheel flats or dipped

joints) were simulated for Benchmark II, participants were provided with the actual

longitudinal rail head profile for their simulations which can be found in Figure 4.3.

A total of eight runs were requested from the participants:

Simulations 1 to 4: No Irregularity

Simulations 5 to 8: Actual Profile (Geelong to Melbourne)

As mentioned earlier, the participants were supplied with a comprehensive set of

input parameters which will be detailed in further sections of this chapter.

The analysis was to be undertaken around ‘Sleeper C’, where the location of ‘Sleeper

C’ was up to the discretion of the participant. Similar to Benchmark I, the

participants were asked to position ‘Sleeper C’ in a location that would eliminate any

boundary effects their model might experience. Other suggestions that were

provided to the participants included:

• Minimum total run distance of vehicle of 20m;

• Minimum time step of 0.0001 seconds (0.1 milliseconds);

• No contact filtering at wheel/rail interface.

59

4.2.2 Vehicle Parameters

The freight vehicles selected for the Benchmark II exercise consisted primarily of

two bogies with only secondary suspension. The freight vehicles selected for

Benchmark II were:

Standard Gauge – Container Wagon (RQTY Class)

Standard Gauge – Structural Wagon (RKWF Class)

Table 4.3 provides further details on the wagons speed, load and direction of travel.

Table 4.3 Wagon Parameters Sim No. Wagon Gross

Vehicle Mass Speed Destination

1 & 5 RQTY Container 52,000 kg 101.7 km/h Geelong

2 & 6 RQTY Container 78, 000 kg 110.8 km/h Melbourne

3 & 7 RKWF Structural 28,000 kg 75.0 km/h Geelong

4 & 8 RKWF Structural 100,000 kg 83.1 km/h Melbourne

The Benchmark II participants were supplied with the details of the wagons (see

Appendix B) which included the suspension characteristics, dimensions of the

vehicle body and wheel profile contact conditions. The participants were also

supplied with a detailed drawing of the bogies used by both freight wagons. The

wheels on the wagons were assumed to be free of any wheel surface defects.

60

4.2.3 Lara Test Site

The Lara test site is situated along the Melbourne – Geelong standard gauge

(1435mm) railway line in Victoria, Australia.

The instrumentation at Lara is managed by the Institute of Railway Technologies

(IRT) and the details of the Lara field set up can be found in Appendix B.

Accelerometers and strain gauges were applied to the sleepers and rails at various

locations.

The sleeper used for analysis (Sleeper C) was fully instrumented with strain gauges

and accelerometers so that the full response of the sleeper to a passing train could be

monitored. Figure 4.1 depicts the layout of the field instrumentation at Lara.

Figure 4.1 Lara Test Site, Melbourne to Geelong Track Line, Victoria

The calibration factors used for the strain gauges and accelerometers were supplied

by IRT to convert the ‘raw data’ which had been measured in millivolts (mV).

Details of the calibration factors can be found in Appendix C.

61

4.2.4 Track Parameters

The railway track at Lara is constructed with AS60 kg/m rail on concrete sleepers

resting on ballast. A capping layer consisting of road base material was also

constructed under the ballast.

At the time of data collection, the ballast was in good condition and the drainage

within the vicinity of the data collection equipment was excellent. The visual

inspection of the site yielded no defects in the ballast or subgrade within the

immediate area of the test sleeper.

No defects (such as squats or rolling contact fatigue) were detected on the rails’

running surfaces. The track is plain line track with no indication of any cross cant

issues, as recorded by the track recording car.

Figure 4.2 Typical example of a cross section of railway track at Lara.

Table 4.4 provides a general detail of the components that the track was constructed

with. More detailed information for the track structure can be found in the

Benchmark II instructions in Appendix B.

Subballast (Capping Layer)

Ballast

Formation (Subgrade)

Rails, Fasteners & Pads Sleeper Gauge 1435 mm

62

Table 4.4 Track Components Component Description

Gauge 1435mm (1505mm rail centres)

Rail AS 60kg/m

Fastener Pandrol ‘e’ clip

Rail Pad 7.5mm HDPE (No. 651840) Insulating Biscuits (No. 55088)

Sleeper Concrete (30 tonne axle load rated) with 680mm spacings

Ballast 200mm (below the sleeper base) (53mm Basaltic Rock)

Subballast 150mm (Crushed rock/scoria)

Formation Medium Stiffness

The participants of Benchmark II were supplied with the longitudinal track profile

collected with the Corrugation Analysis Trolley (CAT) (Grassie, 2004).

Approximately 11m section of rail longitudinal profile over Sleeper C was selected

and it was assumed that both rails were symmetric in profile.

The participants were required to use Profile 1 (Figure 4.3 (a)) for Simulation 6 and

8, whilst Profile 2 (Figure 4.3 (b)) was to be used for Simulation 5 and 7.

-4

-3

-2

-1

0

1

2

3

4

5

-0.16 0.52 1.2 1.88 2.56 3.24 3.92 4.6 5.28 5.96 6.64 7.32 8 8.68 9.36 10.04 10.72

(m)

(mm

)

Geelong Melbourne

Figure 4.3 (a) Profile 1 – To Melbourne (UP Direction)

Slee

per C

63

-4

-3

-2

-1

0

1

2

3

4

5

-0.46 0.22 0.9 1.58 2.26 2.94 3.62 4.3 4.98 5.66 6.34 7.02 7.7 8.38 9.06 9.74 10.42 11.1

(m)

(mm

)

GeelongMelbourne

Figure 4.3 (b) Profile 2 – To Geelong (DOWN Direction)

The location of the instrumented Sleeper C was located at 5.28m for Profile 1 and

5.66m for Profile 2. As mentioned previously, the train wheels were assumed to

have no irregularities.

4.2.5 Wheel/Rail Properties

As mentioned previously, approximately 11m of rail longitudinal profile was

recorded over the instrumented sleeper (Sleeper C) using the Rail Corrugation

Analysis Trolley known as the ‘Rail CAT’ (Grassie, 2004).

The participants were requested to assume the wheel was free of any surface defect

and was perfectly round.

4.2.6 Requested Simulation Outputs

Participants were requested to provide a number of simulations for comprehensive

comparisons against the other participants’ models and against the field data. Table

4.5 details the simulations and output that were requested of the participants.

Slee

per C

64

Table 4.5 Requested Output Parameters Code Output Parameter Unit

A Normal contact force between the wheels and rail kN

B Shear force in the rail at mid span before sleeper C kN

C Vertical acceleration of the rail at the mid span before sleeper C m/s2

D Vertical acceleration at the end of sleeper C m/s2

E Vertical acceleration at the mid span of sleeper C m/s2

F Bending moment at the rail seat of sleeper C kNm

G Bending moment at the mid span of sleeper C kNm

4.2.7 Vehicle Submodels

All the models with the exception of the NUCARSTM model represented the vehicle

with a single bogie with two wheel masses and a sideframe mass, including a

primary suspension element but no secondary suspension elements.

NUCARSTM was the only model that represented the vehicle with two bogies,

including primary and secondary suspension elements.

All six models either assumed or adopted symmetry of loading about the track centre

line. All components in all vehicle submodels were represented as rigid bodies with

linear suspension elements.

4.2.8 Wheel/Rail Interface Submodels

NUCARSTM uses a ‘Real Time Wheel Rail’ contact model that was developed in

house at TTCI. This feature allows for the actual wheel/rail contact geometry to be

computed at each integration time step continuously during the simulation. The local

deformation at the contact point of rail and wheel takes account of the contact

geometry and contact forces.

65

The remaining models are time domain models and allow non-linear Hertzian contact

conditions. The ‘lift off’ of the wheel from the rail is modelled by setting the tension

stiffness of the wheel/rail contact according to Hertz Theory to zero. The wheel/rail

contact in these models is represented by a single point and no filtering was

undertaken to allow for consideration of the actual shape of the contact patch.

4.2.9 Track Submodels

The models that participated in Benchmark II were based on different theories of

mechanical behaviour for the different components that made up the track structure.

Table 4.6 provides brief details on the theories of mechanical behaviour that were

used by each model.

Table 4.6 Theories of Mechanical Behaviour used in Models Models Element

DARTS DIFF DTRACK NUCARS SUBTTI VIA

Boundary Conditions

Fixed at rail ends

Fixed at rail ends

Fixed at rail ends

Fixed at rail ends

‘Ring” Fixed at rail ends

Rail Timo* Timo Timo Euler Timo Timo Rail Pad 1 Spring

1 Damper 1 Spring

1 Damper 1 Spring

1 Damper 1 Spring

1 Damper 1 Spring

1 Damper 1 Spring

1 Damper Sleeper Timo Timo Timo Euler Timo Timo Ballast Winkler Subballast Timo Subgrade Winkler

Winkler Winkler Winkler Half Space Winkler

*Timo = Timoshenko Beam

All the with the exception of SUBTTI assumed fixed end boundaries of the simulated

portion of track. SUBTTI uses a ‘Ring’ type model that allows for the model to

represent the track as a continuous loop simulating an infinite length.

66

4.3 Benchmark II Results

The Benchmark II results supplied by the participants are graphically presented in

this section and were processed through Microsoft Excel. Commentaries associated

with the results are also provided in this section.

4.3.1 Output Parameters

Although seven simulations were requested of the participants, only Simulations 1

and 5 (‘ideal’ versus ‘arbitrary’ wheel/rail conditions) will be analysed in this section

to demonstrate the process applied to all the simulations.

The output parameters that will be examined here are found in Table 4.7. The results

of the remaining simulations can be found in Appendix C.

Table 4.7 Output Parameters Presented Code Output Parameter Unit

A Normal contact force between the wheels and rail kN

B Shear force in the rail at mid span before sleeper C kN

C Vertical acceleration of the rail at the midspan before sleeper C m/s2

F Bending moment at the rail seat of sleeper C kNm

G Bending moment at the mid span of sleeper C kNm

4.3.2 Normal Contact Force Between Wheel/Rail

The two graphs in Figure 4.4 illustrate the differences between the models’ outputs

of the wheel/rail contact forces for (a) ‘ideal’ and (b) arbitrary longitudinal rail

profiles allowing for a detailed comparison. As can be seen, there is no consensus

amongst the models for either case.

67

1A - Normal Contact Force Between Wheel and Rail

40

50

60

70

80

90

0.300 0.330 0.360 0.390 0.420 0.450 0.480 0.510 0.540 0.570 0.600 0.630

Time (s)

Con

tact

For

ce (k

N)

DARTSDIFFDTRACKNUCARSSUBTTIVIA

Sleeper C

Figure 4.4 (a) Wheel/Rail Contact Force for Leading Wheel ‘Ideal’ Rail



0

10

20

30

40

50

60

70

80

90

100

110

0.260 0.290 0.320 0.350 0.380 0.410 0.440 0.470 0.500 0.530 0.560 0.590 0.620 0.650

Time (s)

Con

tact

For

ce (k

N)

-5

0

5

10

15

20

25

30

35

40

45

Dep

th (m

m)

DARTSDIFFDTRACKNUCARSSUBTTIVIAProfile 2, To Geelong

Sleeper C

Figure 4.4 (b) Wheel/Rail Contact Force for Leading Wheel for Arbitrary Rail Longitudinal Profile

In Figure 4.4 (a) the SUBTTI model showed the largest changes in magnitudes for

contact force for both the ‘ideal’ and arbitrary longitudinal rail profile. It is

questionable whether the results of SUBTTI would be reliable given that the peak to

peak variation of its wheel/rail contact force in Figure 4.4(a) is much larger than

would be expected by experienced rail engineers.

68

As mentioned earlier, the NUCARSTM model is capable of varying the geometry of

the contact patch. The results in Figure 4.4 show that this built-in feature of

NUCARSTM still produces results that are not too different to most of the other

models which do not possess this ability. This finding reinforces the statement made

by Grassie (1996) questioning the value of developing models with increasing

complexity as the unknowns in the systems and inherent variability of track may

overwhelm such complexities and hence not produce meaningful results.

Unlike in Benchmark I (Steffens 2005) where DTRACK’s wheel/rail contact force

was significantly lower than the other benchmarked models, the revised version of

DTRACK produced results that were very comparable to the other models as seen by

the graphs in Figure 4.4.

The DARTS and VIA models produced results that were similar to each other when

the peak to peak values were compared for the ‘perfect’ track case in Figure 4.4 (a).

However the phases of the DARTS and VIA models were out of phase with one

another as seen in Figure 4.4 (a)

There would be consequences to the user of any of the models, seeing there is such

variability in the estimation of the magnitudes of wheel/rail contact force. A major

consequence would be uncertainty in the determination of rail head stresses, rolling

contact fatigue growth and grinding maintenance estimation. Engineering decisions

would therefore be very difficult due to the level of variability in output of the

models.

69

4.3.3 Shear Force in Rail at Midspan

The graphs in Figure 4.5 (a) and (b) show the shear force in the rail for the ‘ideal’

and arbitrary longitudinal rail profiles respectively.

1B - Shear Force in Rail at Midspan Before Sleeper C

-40

-30

-20

-10

0

10

20

30

40

0.360 0.390 0.420 0.450 0.480 0.510 0.540 0.570 0.600

Time (s)

Shea

r For

ce (k

N) DARTS

DIFFDTRACKNUCARSSUBTTIVIA

Sleeper C

Figure 4.5 (a) Shear Force in Rail for ‘Ideal’ Rail Longitudinal Profile


-60

-50

-40

-30

-20

-10

0

10

20

30

40

50

60

0.380 0.410 0.440 0.470 0.500 0.530 0.560 0.590

Time (s)

Shea

r For

ce (k

N) DARTS

DIFFDTRACKNUCARSSUBTTIVIALARA Field Data

Sleeper C

Figure 4.5 (b) Shear Force in Rail for Arbitrary Rail Longitudinal Profile

70

It should be noted that the shear force of the field data in Figure 4 (b) is not a

representation of the shear force but a measure of the wheel/rail force, so its

maximum value should be equal to the peak-peak change in shear force of the

models.

From the graphs in Figure 4.5 it is clear that there are issues with DTRACK in the

shape and magnitude of the shear force trace and there was no clear explanation for

the reasoning behind this odd behaviour in DTRACK.

The original author (Cai) was contacted about the matter and a further revision of

DTRACK should have this problem corrected. Although the shear force in the rail

has some importance, it is not a critical parameter that would be investigated when

analysing track.

The NUCARSTM models simulation in Figure 4.5 shows that the model produced the

largest magnitudes of the shear force in the rail for both the ideal and actual

longitudinal rail profiles. Of particular interest is the comparison between

NUCARSTM and the Lara field data in Figure 4.5 (b). NUCARSTM produced a result

that was almost double of the results of Lara and was also significantly higher than

the results of the other models (such as DARTS and VIA).

The DARTS, DIFF, SUBTTI and VIA models produced shear forces that were

similar to each other for both the ideal and actual rail longitudinal profiles. When

compared to the Lara field data in Figure 4.5 (b), the peak to peak shear force result

of these models was almost equivalent with the maximum force of the Lara result.

71

4.3.4 Vertical Acceleration of the Rail at Midspan

Figure 4.6 (a) and (b) show the vertical acceleration of the rail at midspan before

Sleeper C for the ‘ideal’ and arbitrary profiles respectively. Figure 4.6 (b) also

compares the models’ outputs against the Lara field data.

1C - Acceleration of Rail at Midspan Before Sleeper C

-40

-30

-20

-10

0

10

20

30

40

0.450 0.455 0.460 0.465 0.470 0.475 0.480 0.485

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C

Figure 4.6 (a) Vertical Acceleration of the Rail at Midspan before Sleeper C for

‘Ideal’ Longitudinal Rail Profile


-500

-400

-300

-200

-100

0

100

200

300

400

500

0.440 0.450 0.460 0.470 0.480 0.490 0.500

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C

Figure 4.6 (b) Vertical Acceleration of the Rail at Midspan before Sleeper C for

Arbitrary Longitudinal Rail Profile

72

The two graphs in Figure 4.6 clearly show that there is enormous disparity amongst

the results with each of the models having different outputs for the magnitudes and

phases of the rail acceleration.

The results produced by the SUBTTI model for the ideal longitudinal rail profile in

Figure 4.6 (a) show the most interesting accelerations when compared to the other

models outputs. The SUBTTI results in Figure 4.4 (a) show extremely large and

variable magnitudes for the acceleration of the rail. The author of SUBTTI

(Gertsberger) was made aware of the problems associated with the SUBTTI results,

however no responses from the author were received by the writer regarding the

results.

The DARTS results for rail acceleration for both the ideal and actual rail longitudinal

profile cases in Figure 4.6 show very little changes in acceleration. The results of

DARTS for the actual rail profile scenarios in Figure 4.6 (b) is not seen in the results

of the other models and therefore a good comparison with the results of Lara and the

other models could not be made.

If the peak magnitudes of accelerations within the Lara data were compared to the

peak magnitudes of accelerations of the DIFF, DTRACK, NUCARSTM, SUBTTI and

VIA model outputs in Figure 4.6 (b) a correlation between the Lara and models

results can be made which shows that the models can still be very reliable in the

calculation of rail accelerations.

The acceleration of the rail is the only acceleration parameter in Benchmark II that is

shown in this chapter. The results of the other acceleration parameters can be found

in Appendix D.

73

4.3.5 Bending Moment at the Rail Seat of Sleeper

Figure 4.7 (a) and (b) presents the bending moment of the rail seat for both the

‘ideal’ and arbitrary rail longitudinal profiles.

1F - Bending Moment at Rail Seat of Sleeper C

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.350 0.380 0.410 0.440 0.470 0.500 0.530 0.560 0.590 0.620 0.650

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C

Figure 4.7 (a) Sleeper Bending Moment at Rail Seat for ‘Ideal’ Longitudinal Rail

Profile


-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

0.350 0.380 0.410 0.440 0.470 0.500 0.530 0.560 0.590 0.620 0.650

Time (s)

Ben

ding

Mom

ent (

kNm

)

DARTSDIFFDTRACKNUCARSSUBTTIVIALARA Field Data

Sleeper C

Figure 4.7 (b) Sleeper Bending Moment at Rail Seat for Arbitrary Longitudinal Rail

Profile

74

It should be noted that the original results supplied by Delft University of

Technology for DARTS originally contained a time shift error in the simulation of

the bending moment of the rail seat. The authors of DARTS (Esveld and Kok) were

notified of the error and informed the writer that the error was being corrected at the

time of writing the thesis. Therefore the original DARTS results with the time shift

error was not presented and instead, the time has been shifted by the writer to be in

alignment with the other results for uniformity.

The NUCARSTM model produced the largest magnitude of the sleeper moment for

the two different cases of longitudinal rail profile. When compared to the results of

the other models, NUCARSTM was quite excessive in the estimation of bending

moment at the sleeper rail seat, for example in Figure 4.7 (a) DARTS and VIA

produced a bending moment of approximately 2kN.m whilst NUCARSTM was over

4kN.m which is double that of DARTS and VIA.

The SUBTTI model produced interesting bending moment plots. The SUBTTI plots

for rail seat bending moment in both rail profile scenarios showed a small amount of

sleeper vibration of approximately 60Hz in the results, which was also evident in the

results of DTRACK.

DTRACK produced a bending moment magnitude that was comparable with the

other models and in Figure 4.7; however the profile of the bending moment is of

concern as there are a number of large peaks and dips in the bending moment profile.

This bending moment profile was not seen in the results of the other models and the

author (Cai) has been notified of the problems.

The Lara field data is presented in Figure 4.7 (b) is for information, but is not

necessarily a measure of the actual bending moment of the rail seat, as the actual

location of the strain gauge on the sleeper could not be determined from the

information supplied by the Railway Technical Institute (company responsible for

maintaining equipment at Lara); an accurate determination of the bending moment

could not be undertaken.

75

4.3.6 Bending Moment at the Midspan of Sleeper

The graphs in Figure 4.8 (a) and (b) show the outputs from the models for the

bending moment at the sleeper centre for the ‘ideal’ and arbitrary longitudinal rail

profiles respectively.

1G - Bending Moment at Midspan of Sleeper C

-3.5

-2.5

-1.5

-0.5

0.5

1.5

2.5

0.300 0.330 0.360 0.390 0.420 0.450 0.480 0.510 0.540 0.570 0.600 0.630 0.660 0.690

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C

Figure 4.8 (a) Bending Moment at Sleeper Centre for ‘Ideal’ Rail Longitudinal

Profile


-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

0.350 0.380 0.410 0.440 0.470 0.500 0.530 0.560 0.590 0.620 0.650

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C

Figure 4.8 (b) Bending Moment at Sleeper Centre for Arbitrary Rail Longitudinal

Profile

76

The Lara field data presented in the graph in Figure 4.8 (b) is the actual bending

moment of the sleeper centre. Unlike the rail seat bending moment presented in

Section 4.3.5 Figure 4.7 (b) the location of the strain gauge at the sleeper centre was

known precisely and therefore an accurate calculation of the bending moment could

be made.

The SUBTTI results for the sleeper centre bending moment are the most striking for

both rail profile scenarios in Figure 4.8, in that the model produced by far the

smallest magnitudes for sleeper centre moment.

NUCARSTM produced a very curious result for the sleeper centre bending moment.

The results of the NUCARSTM model in Figure 4.8 (b) are associated with a lot of

‘noise’ which was not seen in the rail seat bending moment results in Figure 4.7 (b).

No explanation was supplied from TTCI for these results and no clear explanation

could be derived by the writer. Furthermore, the mean peak magnitude of the

NUCARSTM data for the sleeper centre bending moment was significantly smaller

than the mean peak moment of most of the other models (except SUBTTI) and the

Lara field data.

A good agreement between the DTRACK model and the field data can be seen in

that the mean peak moments are approximate to those of the Lara field data. As with

the results for the bending moment at the midspan of the sleeper, there are many

peaks and dips associated bending moment profile and the author (Cai) again was

notified of the problem.

The results produced by the DARTS, DIFF and VIA were approximate to one

another in the calculation of the mean peak magnitudes for the sleeper centre bending

moment as seen in Figure 4.8. When the results of DARTS, DIFF and VIA were

compared to the Lara field data in Figure 4.8 (a) the models were conservative in the

estimation of the magnitude of sleeper centre bending moment.

77

4.4 Summary

Benchmark II compared a number of international track dynamic models outputs

against each other and against field data collected at Lara on the Melbourne to

Geelong standard gauge railway track in Australia. Benchmark II also provided a

good opportunity to test and compare the outputs produced by the revised DTRACK

against the other models and against field data.

Within the Benchmark II exercise described in this chapter, the outputs of six models

were examined. All the models simulated track and train operating conditions that

were spelled out in a common specification.

The DARTS model is available for purchase through Esveld Consulting Services

without ongoing technical support. Benchmark II has shown that the results

produced by DARTS are very approximate to the field data and to the outputs of

most of the remaining models in terms of force magnitudes. Although DARTS had

some issues relating to the time domain for the bending moment parameter, the error

was being fixed by the authors (Esveld and Kok) of DARTS at the time of writing.

Benchmark II has shown that the results of the DIFF model was also very

approximate to the results of the other dynamic models and the field data for specific

parameters in the Benchmark II exercise. However, for the bending moments of the

sleeper, DIFF consistently produced results that were conservative in peak

magnitudes when compared to the field data and other models.

The DTRACK model will soon be available for commercial release. The DTRACK

model’s results showed that the maximum values within the results are relatively

consistant with the field data and other track dynamic models. However, Benchmark

II has shown the DTRACK has a few issues with the outputs of the model such as the

shear force in the rail and many peaks and dips in the magnitudes of the bending

moment of the sleeper. The author of DTRACK has been notified of the problems

and was addressing them at the time of writing.

78

The NUCARSTM model that was used for the Benchmark II exercise was a beta

version that is not yet available commercially. NUCARSTM was by far the most

complex of the dynamic models that participated in Benchmark II and produced

certain results that were questionable such as the shear force of the rail in Figure 4.5

(b) and the sleeper centre bending moment in Figure 4.8 (b).

The results of SUBTTI were highly variable compared to the Lara field data and the

other models, especially in the wheel/rail contact force in Figure 4.4 (a), acceleration

of rail in Figure 4.6 (a) and the sleeper centre bending moment in Figure 4.8. The

variability within the SUBTTI results in Benchmark II raises questions about the

reliability of the SUBTTI model.

The VIA model produced results that were consistently in approximation with the

field data and the other models for all the specified output parameters in Benchmark

II.

Table 4.8 summarises the Benchmark II’s participant models correlation with each

other and the Lara field data. The table is intended to provide a general overview

each models correlation with one another.

Table 4.8 Correlation between models and Lara field data DARTS DIFF DTRACK NUCARS SUBTTI VIA LARA

DARTS

DIFF

DTRACK

NUCARS

SUBTTI

VIA

= if at least 2 of the presented output parameters of one model were within 10% of the other models = if 3 or more of the presented output parameters of one model were within 10% of the other models

= if 2 or more of the presented output parameters of the model were more than 10% different from the other models

79

Table 4.9 Summary of Results

Peak Value of Output Parameter L

ara

DA

RT

S

DIF

F

DT

RA

CK

NU

CA

RS

SUB

TT

I

VIA

Average wheel/rail contact force (kN)

- 65.05 63.76 63.75 63.82 62.56 63.70

Shear Force (kN) 77.02* -10.55*

30.87 -24.65

29.95 -38.73

16.65 -17.65

50.68 -47.88

18.56 -42.76

28.49 -36.21

Average rail midspan acceleration (m2/s)

379.42 -391.13

13.55 -17.96

268.78 -179.55

172.44 -196.13

246.97 -292.23

412.88 -285.54

377.51 -574.29

Bending moment at midspan of sleeper (kN.m)

0.05 -2.82

0.14 -3.43

0.18 -4.07

2.11 -3.41

2.26 -3.07

0.02 -0.42

0.26 -4.51

Bending moment at sleeper rail seat (kN.m)

1.21** -0.14**

2.42 -0.10

4.42 -0.24

5.78 -1.74

6.54 -0.04

5.38 -0.22

3.20 -0.25

* Peak wheel/rail force which should be equivalent to the peak-peak change in shear force of the models.

**Lara data for bending moment at rail seat is incorrect due to faulty equipment.

80

CHAPTER 5

Measurements of Wheel/Rail Forces

5.1 Introduction

To achieve the ultimate goal of a limit state design process for railway design, a

comprehensive collection of wheel/rail force data will be required to determine the

design load environment of the railway track during its service lifetime.

This thesis will only examine the impact forces caused by defects at the wheel

interface for two main reasons:

1. Wheel defects occur at random and have a high probability of occurring.

2. Impact events caused by wheel defects are not localised (such as dipped

joints) and can impact at random along a given section of railway track.

This chapter discusses the equipment used to collect the wheel impact data and the

methodology used to process the data and to present it. An interpretation and

evaluation of the data is also presented and will form the basis of establishing the

design load environment for railway track.

81

5.2 Wheel Condition Monitor (WCM) Systems

The effects of wheel impacts on the track structure can be quite severe due to the

range of magnitudes and frequencies of the wheel impact forces large as illustrated in

Chapter 2. This section will examine the equipment used to collect the wheel impact

data, the processing of the data and the validation of the data collection equipment.

5.2.1 Teknis Wheel Condition Monitoring System (WCM)

Most railway organisations in Australia have installed early detection systems that

are capable of detecting and measuring the severity of wheel defects as a

preventative measure against potential damage to the railway track.

The wheel condition monitoring system used in this research is a commercially

available product known as the Teknis Wheel Condition Monitoring System (WCM)

which integrates specialized monitoring equipment into a single database (Teknis,

2005).

The WCM of Queensland Rail was the only systems that were used in this research

as the writer was unable to obtain WMS data from other rail organisations.

5.2.2 Wheel Condition Monitoring Systems

The Teknis Wheel Condition Monitoring system is based on a series of

accelerometers and strain gauges that are mounted on the rail and measure the

motion of the rail as a vehicle traverses over the railway track.

82

There are two main reasons why the Teknis WCM uses accelerometers in addition to

the traditional strain gauges to measure the force induced into the track (Teknis,

2005):-

1. Strain gauges do not capture 100% of the rolling wheel surface. This is a

disadvantage in situations where the axle loads are varied, because the WCM

system may miss a defect on a wheel when the wagon is empty, however it

may detect the same defect on the same wagon when it is loaded; and

2. The use of accelerometers and strain gauges provided a continuous 100%

coverage of the wheel circumference and therefore allowed for multiple

defect identification on a wheel surface.

The Teknis WCM systems used for this research are based at Queensland Rails

Braeside and Raglan sites which are located on the Goonyella System and the

Central Line respectively; the location of these sites can be found in Figure 5.1 (a)

and (b) respectively.

Figure 5.1 (a) Teknis WCM Braeside Site

83

Figure 5.1 (b) Teknis WCM Raglan

The calibration of the systems installed on Queensland Rails network is undertaken

by Teknis. The photo in Figure 5.2 shows the Teknis WCM system at Raglan.

Figure 5.2 Teknis WCM Hardware (Teknis, 2005)

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84

5.2.3 Wheel Condition Monitoring Database (WCM Database)

The information collected from the Teknis WCM is stored on a server where the data

can be retrieved via Microsoft Access 2000. The information stored on the WCM

database is ‘read only’ and therefore cannot be altered by users accessing the

information. However, the data can be downloaded into a text or Excel file for

analysis and manipulation by the users.

The information on the WCM database records many parameters such as maximum

impact force (kN), speed of vehicle (km/hr), axle load (t) and direction of travel. It

should be noted that not all the information on the database is usable until the data is

filtered, which will be explained later.

The WCM database only stores information for ‘tagged’ wagons within a train

consist and will not record any information for trains that are ‘untagged’. A ‘tagged’

wagon is where a train has an identification card installed on the wagon which allows

the Teknis WCM system to identify that wagon and associate any defect recorded by

the system back to the wagon with the defective wheel.

A general schematic in Figure 5.3 shows how the WCM and database are linked

together.

85

Figure 5.3 Overview of the Teknis System (Teknis, 2005)

Another important feature of the Teknis system is the display of the impact forces in

the database. The program automatically normalizes all impact readings collected

from the vehicles to the vehicles loaded condition. For example, when a good wheel

passes over the WCM, the database will record the wheel/rail force as zero,

regardless of its axle load. However, if a wheel defect produces a reading of 100kN,

than it would translate to an impact load of 100kN only.

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86

5.3 Processing of Data

The wheel impact data was collected for one week out of every month for the period

between March 2005 to March 2006 for the following reasons:-

• A twelve month period would account for seasonal variations in the track

structure which may possibly affect the magnitudes of the impact forces;

• A week out of every month was determined to be statistically significant

enough to represent a general distribution of impact forces; and

• The weeks targeted for the data collection did not include any periods of

major maintenance works that would affect the traffic volumes.

Figure 5.4 provides an example of the data downloaded from the WCM database.

Figure 5.4 Example of Entries in Teknis WCM Database

87

The WCM database in Figure 5.4 shows the direction of the train as seen in the

yellow box of the top left hand corner of Figure 5.4, and indicates whether the train

was either full or empty. For example the yellow box in the top left hand corner it

reads ‘Mine on DM Braeside’ which means that the train was going down from the

mine to the port at the Braeside site. This information was used to separate the data

into full and empty trains to investigate whether the impact force magnitudes were

affected by the gross vehicle mass or unsprung mass of the vehicle.

The ‘x’ in the third row of the WCM database in Figure 5.4 indicates an erroneous

entry in the data. Any such entries in the raw data were filtered out in Microsoft

Excel and were counted as null.

The processing of the data was undertaken using Microsoft Excel where the data was

summarised and graphed for analysis and interpretation. The following section

presents the graphs produced in Microsoft Excel and provides a commentary and

interpretation of the data that ultimately was used in this research for the formation

of a limit state design philosophy for railway track (see Chapter 6 and 7).

88

5.4 Presentation and Interpretation of Data

The data is presented in this section in a way that informs the development of a limit

state philosophy for railways. Although only a single unique operational condition is

presented, the methodology developed can be applied to other railway operational

environments.

It should be noted that the two separate sites, Braeside and Raglan, operated different

types of rollingstock. The Braeside site operates wagons ranging from 100-106

tonnes (GVM) and the Raglan site operating wagons ranging from 90-104 tonnes

(GVM). Despite the various types of rollingstock, the net vehicle masses of all the

wagons are very similar according to information supplied by the operator of the

trains on both sites.

The red line in Figure 5.6 to 5.8 is the impact force limit prescribed by the Defined

Interstate Network Code of Practice (Volume 5, Part 2 - Section 8, 2002). Although

the Braeside and Raglan sites do not fall under the Defined Interstate Network, the

limitation can still be applied to this situation as a point of reference.

As the data collected represented only a heavy haul situation, the data was separated

into two different scenarios: empty and full wagons. This separation permits

determination as to whether the magnitude of impact force was dependent on the

mass of the vehicle.

The following section presents the impact force distributions and provides an

interpretation of how the distributions will form the basis of a limit state design for

railway track.

89

5.4.1 Impact Force Distributions

The data collected from the Braeside and Raglan WCM was processed in Microsoft

Excel, as shown in the spreadsheet in Figure 5.5.

Figure 5.5 Example of Processed Data from Excel

The graph in Figure 5.6 is an impact force distribution graph that was derived from

the processed data of the WCM database for the empty wagons operating at both the

Braeside and Raglan sites. It should be noted that the graph in Figure 5.6 is plotted

on a logarithmic vertical axis and plots the data shown in Figure 5.5.

90

Impact Forces VS No. of Wheels (Empty)Heavy Haul Braeside & Raglan 2005-2006

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heel

s

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Allowable Impact Force(Code of Practice)

Figure 5.6 Impact Forces VS No. of Wheels (Empty)

Rollingstock operating on the Braeside and Raglan lines are maintained using the

same wheel maintenance standard and therefore, it would be assumed that the impact

force distributions would be similar to one another.

However, from the graph in Figure 5.6 it is clear that Raglan has a larger number of

impact forces greater than 130kN than at Braeside. This may suggest that the

intervention strategy for wheel maintenance at Raglan may not me as rigorous as that

at Braeside as both operators nominally maintain their wheels to the same standard.

It should be noted that Braeside operates larger train units and therefore will have

more wheels passing over the site compared to Raglan. Therefore for better

comparison between the two sites, the distribution graph in Figure 5.6 has been

normalised as shown in Figure 5.7. The normalised graph in Figure 5.7 represents

the percentage of the total wheel impact forces collected at Braeside and Raglan.

91

Impact Forces VS No of Wheels (Normalised)Heavy Haul Braeside & Raglan 2005-2006 (Empty)

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heel

s (x

106 )

BraesideRaglan

Line of Best Fit

Figure 5.7 Impact Forces VS No. of Wheels (Empty), Normalised

From the normalised graph in Figure 5.7, the Raglan data again shows a higher

percentage of impact forces greater than 130kN than at Braeside, despite the same

wheel maintenance standard, reinforcing the notion that Raglan may have a less

rigorous wheel maintenance intervention strategy.

The line of best fit within the graph in Figure 5.7 was drawn by the writer because

the mathematically derived linear regression lines did not match the data well. The

lines of best fit represent the measured impact force for Braeside and Raglan for a

one year period.

Figure 5.8 shows the impact force versus the number of wheels for the full wagon

scenario for both the Braeside and Raglan sites. The graph in Figure 5.8 is again

plotted on a logarithmic scale.

92

Impact Forces VS No of Wheels (Full)Heavy Haul Braeside & Raglan 2005-2006

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Impact Forces (kN)

No.

of W

heel

s

BraesideRaglan

Allowable Impact Force(Code of Practice)

Figure 5.8 Impact Forces VS No. of Wheels (Full)

The graph in Figure 5.8 again shows that the Raglan site has a higher number of

large impact forces when compared to Braeside and as before, shows that Raglan

may have a different wheel maintenance strategy as Braeside.

As in the empty wagon scenario, the graph in Figure 5.8 was normalised for better

comparison between the two sites and is shown in Figure 5.9.

Impact Forces VS No of Wheels (Normalised)Heavy Haul Braeside & Raglan 2005-2006 (Full)

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Impact Forces (kN)

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cent

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heel

s (x

106 )

BraesideRaglan

Line of Best Fit

Figure 5.9 Impact Forces VS No. of Wheels (Full), Normalised

93

A comparison of the impact force distributions in Figure 5.7 (empty wagons) and 5.9

(full wagons) showed that Braeside distribution was not affected much by the

significant difference in weight between the full and empty wagons, where as the

Raglan distribution showed a significant difference. The difference in wheel impact

force distribution at Braeside and Raglan may be due to the speed at which the trains

were travelling through the detection site. The speed of the vehicle is the only

uncontrolled factor when collecting the data and is independently controlled by the

driver, therefore the difference in distribution is most probably caused by driver

behaviour rather than some other factor.

In addition the disparity between the Braeside and Raglan impact force distributions

in Figure 5.7 and 5.9 shows that wheel maintenance strategies are an important factor

in the determination of impact force magnitudes. Therefore the development of a

limit state methodology must take into consideration the effects of changing

operational speeds and maintenance practices.

5.4.2 Effect of Speed on Impact Force Distributions

From the impact force distribution graphs presented in Section 5.4.1, it is evident that

factors, such as speed maybe influencing the distribution of impact forces. This

section will examine the effect of speed, and therefore driver influence, on impact

force distributions from the Braeside and Raglan data.

The graphs in Figures 5.10 show the number of impact forces versus speed

distributions for (a) Braeside and (b) Raglan sites for only empty wagons.

94

No of Impact Forces VS SpeedHeavy Haul Braeside 2005-2006 (Empty)

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Impact Forces (kN)

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Figure 5.10 (a) Impact Force VS Speed - Braeside (Empty)

No of Impact Forces VS Speed

Heavy Haul Raglan 2005-2006 (Empty)

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Figure 5.10 (b) Impact Force VS Speed - Raglan (Empty)

The graphs in Figure 5.10 shows that most of the impact force data was collected

between the speeds of 70-80km/hr, so the graphs do not provide a clear indication on

the effect of speed of the impact force distributions.

Therefore, the two graphs in Figure 5.10 have been ‘normalised’ to represent the

percentage of impact forces occurring at various speeds for clearer comparison, as

95

shown in Figure 5.11. The normalised graphs in Figure 5.11 represent the

percentage of total wheels passing the site at the specified speeds and impact force

band widths.

No of Impact Forces VS Speed (Normalised)Heavy Haul Braeside 2005-2006 (Empty)

0.0000%

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Impact Forces (kN)

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30-4040-5050-6060-7070-8080-90

Figure 5.11 (a) Impact Force VS Speed – Braeside Normalised (Empty)


Heavy Haul Raglan 2005-2006 (Empty)

0.0000%

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Figure 5.11 (b) Impact Force VS Speed – Raglan Normalised (Empty)

The graph in Figure 5.11 (a) show that the proportion of large impact forces

(>150kN) occur more frequently at lower speeds (<60km/hr) than at higher speeds

96

which suggests that the drivers may be changing the speeds of the trains according to

the ‘drivability’ of the train. The graph in Figure 5.11 (b) also shows that for impact

forces between the 150kN – 200kN are occurring more frequently at lower speeds

(<60km/hr) than at higher speeds which again shows that drivers are influencing the

impact forces.

Upon further investigation into how the train drivers are influencing the impact force

distributions, the author found that the operator requires its train drivers at Braeside

and Raglan to slow down the vehicle when they detect that the normal ‘drivability’

of the vehicle has changed. The changes in speed have a significant influence on the

impact force distributions presented in Section 5.4.1 and must be taken into

consideration in the development of limit state factors.

The two graphs in Figure 5.12 (a) and (b) show the impact force versus speed

distributions for the full wagon scenario at Braeside and Raglan.

Both graphs in Figure 5.12 again show a distribution with the majority of forces

occurring at 70-80km/hr and 60-70 km/hr for the Braeside and Raglan sites

respectively. However, unlike the empty scenario, there is a distinctive ‘gap’ at the

100-110kN impact force bandwidth which is evident in both data sets. The gap in

the data might be due to the Teknis WCM system not recording properly within this

bandwidth, though there is no other evidence of this.

97

No of Impact Forces VS SpeedHeavy Haul Braeside 2005-2006 (Full)

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Figure 5.12 (a) Impact Force VS Speed – Braeside (Full)


Heavy Haul Raglan 2005-2006 (Full)

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Figure 5.12 (b) Impact Force VS Speed – Raglan (Full)

As with the empty wagon scenario, the graphs in Figure 5.12 have been normalised

for better comparison of the impact forces versus speed as seen in Figure 5.13.

98

No of Impact Forces VS Speed (Normalised)Heavy Haul Braeside 2005-2006 (Full)

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Figure 5.13 (a) Impact Force VS Speed – Braeside Normalised (Full)

No of Impact Forces VS Speed (Normalised)

Heavy Haul Raglan 2005-2006 (Full)

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Figure 5.13 (b) Impact Force VS Speed – Raglan Normalised (Full)

From the graphs in Figure 5.13, it is very difficult to draw any firm conclusions from

the distributions as the large impact forces (>150kN) are difficult to distinguish,

especially from the graph in Figure 5.13 (a). Therefore the graphs in Figure 5.13

have been ‘expanded’ out for better analysis and are seen in Figure 5.13 (c) and (d).

99

No of Impact Forces VS Speed (Normalised)Heavy Haul Braeside 2005-2006 (Full)

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Figure 5.13 (c) Impact Force VS Speed – Expanded View Braeside Normalised

(Full)

No of Impact Forces VS Speed (Normalised)Heavy Haul Raglan 2005-2006 (Full)

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Figure 5.13 (d) Impact Force VS Speed – Expanded View Raglan Normalised (Full)

From the graph in Figure 5.13 (c) the impact forces are fairly evenly distributed

amongst the range of speeds, which suggests that the drivers at Braeside are

following the operational requirements of adjusting the train speeds when the

‘drivability’ has changed. A comparison of the impact force distributions at Braeside

(c) and Raglan (d) again shows that Raglan has more large impact forces occurring

than at Braeside which reinforces the notion of different maintenance strategies.

100

Figure 5.13 (d) shows that at Raglan, the impact forces occurring between 150kN

and 220kN are occurring more frequently at lower speeds (<60km/hr). This again

suggests that the drivers are changing the speeds of the vehicle to suit the operating

conditions. However, for significant impact forces (>220kN) Figure 5.13 (d) shows

that these impact forces are more likely to occur at higher speeds (>70km/hr). This

may be attributed to the maintenance practices at Raglan rather than the drivers not

following the operators instructions.

From the distributions presented in this section, it is clear that the speed and driver

behaviour may have a significant influence on the wheel impact force distributions.

This could have major implications for the prediction of probability and return

periods of impact forces particularly if the operational speeds of the track were to

change.

5.4.3 Axle Load Distributions

In the development of a limit states methodology, ‘worst case scenarios’ must be

investigated so that defensible loading factors can be determined. The graphs in

Figure 5.14 (a) and (b) show the static axle load of the full wagons that were

collected by the Teknis WCM at Braeside and Raglan respectively. Empty wagons

were not investigated in this section as the axle loads of empty wagons are not the

worst case scenario.

The blue arrows within the graphs in Figure 5.14 show the ‘target pay load’, which is

the payload that the loaders aim to achieve. As mentioned earlier, the Raglan site

operates two different types of wagons and therefore there are two different ‘target

pay loads’.

101

No of Axles VS Axle Load (Full)Heavy Haul Braeside 2005-2006

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f Axl

es

Target Pay Load

Figure 5.14 (a) Number of Axles VS Axle Load – Braeside

No of Axles VS Axle Load (Full)Heavy Haul Raglan 2005-2006

0

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ber o

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es

Target Pay Load 2Target Pay Load 1

Figure 5.14 (b) Number of Axles VS Axle Load – Raglan

From the graphs in Figure 5.14 it is evident that the target pay load is not always

achieved. This distribution of wagon loading must be taken into consideration when

developing a limit state methodology for railway track. The roughly 20% loading

beyond the target can be incorporated into a loading factor for the static axle load

which will be further explained in Chapter 7.

102

5.5 Summary

The impact forces caused by the defects at the wheel interface will be the only data

examined in this thesis for two main reasons. The first is that wheel defects are

random events that have a high probability of occurring and secondly, impact events

caused by wheel defects are not localised (such as dipped joints) and can impact at

random at any point on a given section of railway track.

The commercially available Teknis WCM system was used to collect the wheel

impact data. The system uses a series of accelerometers in addition to strain gauges

to collect the data and is capable of collecting information from 100% of the wheel

surface. Queensland Rail’s WCM system on the Braeside and Raglan sites were

used for analysis. The Braeside and Raglan sites are situated on the Goonyella

System and Central Line respectively.

The graphs presented in this chapter were for a single case (heavy haul traffic) and

are not representative of all operations in Australia. Therefore, it is recommended

that more data be collected with different operational characteristics before reliable

limit state factors can be developed.

Raglan and Braeside operate under the same wheel maintenance standard and

therefore it would be assumed that the impact force distributions of both sites would

be relatively the same. However data has shown that Raglan has a very different

impact force distribution than Braeside, which suggests that the wheel maintenance

strategy at Raglan is different to that at Braeside. Wheel maintenance strategies and

practices can have a considerable influence on the distribution of impact forces.

The impact force versus speed distribution graphs in Figure 5.12 and 5.14 have

shown that not all of the trains were travelling at the same speed and that the train

drivers appeared to be adjusting the speeds of their trains according to the

‘driveability’ of the trains.

103

The number of axles versus static axle load distribution graphs in Figure 5.14 has

shown that the target pay load is not always achieved and a spread of loading of

wagons does occur. The maximum static loads are approximately 20% above the

target load, which can be taken into consideration by incorporating the 20% as a limit

states factor which will be further explained in Chapter 7.

In the development of a limit state design methodology for railway loadings, the

influence of wheel maintenance practices and driver behaviour must be taken into

consideration, because the determination of probability of occurrence and return

periods of the impact forces seem to be influenced by these parameters.

The following chapter examines how the probabilities and return periods are

established by the impact force distributions presented in this chapter. The following

chapter also examines how the impact force distributions can vary due to the effects

of varying parameters and examines the implications for the development of a limit

state design methodology for railway track loadings.

104

CHAPTER 6

Time Analysis of Data

6.1 Introduction

The basis for applying to a limit state design principles to railway track is the

establishing return periods of exceptional impact forces that could compromise the

railway track’s primary function. The data presented in Chapter 5 is used in Chapter

6 for this purpose.

This chapter will how varying speeds, unsprung masses, suspension characteristics

and maintenance practices could affect the impact force distributions presented in

Chapter 5 and the implications for limit state design of railway track.

6.2 Principles for Determining Design Load

The design of railway track in Australia has traditionally been based on empirical

methods as mentioned previously in Chapter 2. The problem with using an empirical

methodology to calculate the wheel/rail force is that it does not take into account the

complex interaction between the rollingstock and track, especially when there are

defects at the wheel/rail interface which can create significant impact forces.

These impact forces caused by defects at the wheel/rail interface can occur at random

along the track structure. Therefore a design methodology that takes into account the

105

risks and return periods associated with these impact forces is needed for a more

realistic assessment of track loadings.

The notion of designing structural elements based on return periods and risk is not a

new concept. Most of the current Australian structural standards such as the

Concrete Structures code (AS3600, 2001) and Steel Structures (AS4100, 1998) are

already based on concepts. Risk based design methods and standards have been

widely accepted in the engineering community.

To establish the probable loading environments, extensive loading data over a period

of time had to be collected. From the data, loading factors could then be developed

to account for exceptional events that were likely to happen. For example, in the

Australian Standard Structural Design Actions (AS1170, 2002) the code provides a

loading factor of 1.5 for ‘live’ load to account for the ultimate limit state for loading

on a structural element.

It would be impractical and uneconomical to design any structure based on the

indeterminate force especially if the structure is not expected to be in service for a

long period of time. Therefore considerations of return periods for exceptional

events during a structures service lifetime would provide a more realistic assessment

of the loadings on a structure.

The concept of return periods has already been adopted in the Australian Standards

series for example, the Structural Design Action – Wind Actions (AS11770.2, 2002)

code allows the designer to choose which appropriate wind actions and combinations

that could be expected during the lifetime of a structure.

Another concept that is not new to structural design is the inclusion of risk analysis

into the design. By designing structures based on risk, the designer can estimate the

reliability of the structure based on the consequences of failure. An example of risk

based design can be found in the current Earthquake Loading Code (AS1170.4

Clause 2.5, 1993). Within AS1170.4 (1993) there are importance factors that

account for the risks associated with certain structures in the event of a post disaster

scenario. For example, important buildings (such as hospitals, bridges etc) have a

106

loading factor of 1.25 to increase the reliability of the structure to resist a post

disaster event.

The design of structures based on loads more representative of reality ensures that

the structure would be better designed for its intended purpose and environment,

leading to a potentially more economical design. However in order to transform the

current Australian Standards Railway codes (AS1085, 2002) to limit state, an

extensive set of loading data is needed to establish the probabilities and risks

associated with the loading environment.

6.3 Establishing Probabilities for Impact Forces

The data presented in Chapter 5 will be used to establish the probabilities and return

periods for impact forces occurring on the railway track test sites. The data from

both Braeside and Raglan will be used as a working example on how the

distributions could be used to establish these probabilities and return periods for a

heavy haul scenario.

The data collected from Braeside and Raglan was for a unique heavy haul scenario

and may be comparable to lines that operate freight and passenger traffic. However

the methodology presented in this thesis can be applied to these other scenarios as it

would be expected that there would be a similar profile for the distribution of impact

forces.

The impact force graphs that were presented in Chapter 5 have shown that the

distributions appeared to be dependent on a series of factors such as speed and

maintenance practices. Of particular importance, Chapter 5 showed that the drivers

seemed to be adjusting their vehicles speeds according to changes in the ‘normal’

driveability of the train which had significant influence on the impact force

distributions. Since driver behaviour was probably modifying the impact force

distributions so that they tended to fit a given pattern, the impact force distribution

graphs in Chapter 5 for full and empty wagons were combined into a single

distribution graph for each of Braeside and Raglan.

107

The graph in Figure 6.1 (a) and (b) are the combined wheel impact forces (for both

full and empty wagons) versus the number of axles for a one year period for the

Braeside and Raglan sites respectively. The graphs in Figure 6.1 also show best fit

trendlines. It should be noted that the y-axes are plotted on a logarithmic scale.

Impact Forces Vs No. of Axles (Combined Full & Empty Wagons)Heavy Haul Braeside 2005-2006

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es

Figure 6.1 (a) Braeside - Impact Forces VS Number of Axles (Full & Empty)

Impact Forces Vs No of Axles (Combined Full & Empty Wagons)Heavy Haul Raglan 2005-2006

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es

Figure 6.1 (b) Raglan - Impact Forces VS Number of Axles (Full & Empty)

108

The equations of the lines of best fit in each of the graphs in Figure 6.1, fit the basic

form of:

cmxy += Equation 6.1

Where, M = Slope of the line,

xym

ΔΔ

=

c = Constant

However, y-axis of the graphs in Figure 6.1 are plotted on a logarithmic scale,

therefore the gradient of the line becomes;

xym

ΔΔ

=)(log Equation 6.2

And the equation of the line for the logarithmic graph becomes;

cmxy loglog += Equation 6.3

or

cmxy += 10 Equation 6.4

Using the Braeside data as an example, Equation 6.4 can be solved by choosing two

points from the Braeside data where x1 = 310, y1 = 1 and x2 = 100, y2 = 10,000 and

calculating the slope of the line, m:

xym

ΔΔ

=)(log

)100310()000,10log1(log

−−

=m

0191.0−=m Therefore, Equation 6.4 becomes;

cxy +−= 0191.010 Equation 6.5

Solving for c by using the coordinates, x1 = 310, y1 = 1, Equation 6.5 becomes; c+×−= 3100191.0101

c+−= 92.51log

92.5=c

Therefore, the equation of the line for Braeside becomes;

109

92.50191.010 +−= xy Equation 6.6 (a)

Using the same methodology, the equation of the line for Raglan becomes;

1.401.010 +−= xy Equation 6.6 (b)

Where, y = Number of wheels in a 12 month period causing a wheel impact of

x kN x = A given impact force in kN (not including wheel/rail static contact

force)

By using these equations, the return periods for impact forces can be estimated and

hence the railway track components can be designed on a more probabilistic basis.

For example, the concrete sleepers at Braeside and Raglan are designed for a 50 year

life span. Using Equation 6.6 (a) and (b) to calculate the expected impact force for a

50 year return period for Braeside and Raglan would be 400kN and 580kN

respectively, for a single impact event.

A methodology for probabilistic design needs to allow for operational factors such as

variations in operational speed or wheel maintenance practices, which have shown to

have a notable influence on the magnitudes of impact forces (see Chapter 5). The

following section develops and examines simulations of various such scenarios.

110

6.4 Consequences to Impact Forces Due to Varying Parameters

Varying parameters such as changes in speed, unsprung mass, suspension

characteristics and maintenance practices may have a significant influence on the

magnitudes of impact forces generated into the railway track. The DTRACK

program is used below to investigate how these varying parameters could affect the

probabilities and return periods of impact forces.

A heavy haul wagon that operated at both the Braeside and Raglan sites was chosen

for the simulations. The simulations were based on two scenarios, of empty and full

heavy haul wagons.

6.4.1 Varying Velocities

It is clear that speed has a significant effect on the magnitudes of impact forces as

shown in the graphs presented in Chapter 5. Speed based simulation was undertaken

using the following shared parameters:-

• Starting at 20km/hr to 120km/hr at 20km/hr increments;

• 5mm, 25mm and 50mm wheel flats were chosen for simulation; and

• Suspension characteristics were constant.

Speeds over 120km/hr were not considered and are improbable for heavy haul trains

within the near future at the time of writing this thesis.

The graphs in Figure 6.2 (a) and (b) show the impact force versus speed for empty

wagons and full wagons respectively, for the prescribed simulations using DTRACK.

111

Impact Force vs Speed(Empty 106t Narrow Gauge Heavy Haul Wagon)

0.00

100.00

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500.00

600.00

0 20 40 60 80 100 120 140

Speed (km/hr)

Impa

ct F

orce

(kN

)

5mm Chord,0.0068mm Depth



Figure 6.2 (a) Empty Wagons – Narrow Gauge with Varying Speed

Impact Force vs Speed (Full 106t Narrow Gauge Heavy Haul Wagon)

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Max

Impa

ct F

orce

(kN

)




Line of Best Fit

Line of Best Fit

Figure 6.2 (b) Full Wagons – Narrow Gauge with Varying Speed

The graph in Figure 6.2 (a) shows that for the empty wagon scenario with small

wheel flats (<5mm), the magnitude of impact forces did respond to the changes in

speed. However, for medium to larger wheel flats, the impact forces decreased in

magnitude with increased speeds over 60km/hr.

112

The graph in Figure 6.2 (a) also reinforces Tunna (1998) which states that increasing

speeds do not necessarily correspond to increasing magnitudes of impact forces. The

axle loads of empty wagons are not too different from the passenger vehicle used in

Tunnas’ (1988) research and are small compared to heavy wagons and are therefore

not representative of normal operations at Braeside and Raglan.

The lines of best fit within the graph in Figure 6.2 (b) were drawn in by the author to

better represent the effects of speed versus impact force magnitude. This was

because at 60km/hr the impact force showed a ‘dip’ for large wheel flats that made

the results difficult to utilise.

The graph in Figure 6.2 (b) again shows that for small wheel flats (<5mm) the

magnitudes of impact forces do not change with speed. However, unlike the empty

scenarios, the magnitudes of impact forces for full wagons increased significantly

with increasing speed. This has major implications for the limit state design of

railway track components as the probability and return periods of impact forces

would change if the operational speeds at Braeside and Raglan were to be increased.

Comparing Figure 6.2 (a) and (b) also shows that the gross vehicle mass has a

significant effect on the magnitude of wheel impact forces. Therefore the full wagon

scenario should be used to predict future changes to operational speeds as it

represents the worst case scenario.

113

6.4.2 Varying Unsprung Mass

Variations in the unsprung mass of a vehicle may have an influence on the wheel

impact forces caused by wheel defects induced into the track. The simulations

presented in this section were undertaken with the following parameters:-

• Heavy haul wagon travelling at 80km/hr;

• A wheel flat size with a 25mm chord;

• Fixed gross vehicle mass; and

• Fixed suspension characteristics.

The graph in Figure 6.3 (a) and 6.3 (b) show the changes of impact force for an

empty and full heavy haul wagons respectively with varying unsprung masses.

Impact Force vs Varying Unsprung Mass(Empty 106t Narrow Gauge Heavy Haul Wagon

@ 80km/hr with 25mm Wheel Flat)

0.00

50.00

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200.00

250.00

-20% -15% -10% -5% 0% 5% 10% 15% 20%

Varying Unsprung Mass

Impa

ct F

orce

(kN

)

Figure 6.3 (a) Empty Wagons – Narrow Gauge with Varying Unsprung Mass

114

Impact Force vs Varying Unsprung Mass(Fully Loaded 106t Narrow Gauge Heavy Haul Wagon


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200.00

250.00

-20% -15% -10% -5% 0% 5% 10% 15% 20%

Varying Unsprung Mass

Impa

ct F

orce

(kN

)

Figure 6.3 (b) Full Wagons – Narrow Gauge with Varying Unsprung Mass

The graph in Figure 6.3 (a) shows that for empty wagons, the impact force increases

a little with increasing unsprung mass. This is because the unsprung mass of an

empty wagon represents a significant portion of the gross vehicle mass. However,

the change in impact force for the empty scenario was only 2.8kN which represented

a change of approximately 3%, this is too small to have any major implications on

the design loads for railways.

The graph in Figure 6.3 (b) shows that changes in the unsprung mass of the full

vehicle caused only a 1% change in the magnitude of the impact forces. In the full

wagons scenario, the unsprung mass represents a very small portion of the gross

vehicle mass and therefore does not any have any great influence on the overall

magnitude of the impact force.

115

6.4.3 Varying Suspension Characteristics

Variations to the suspension characteristics of the vehicle may also change the wheel

impact forces caused by rollingstock. The simulations presented in this section were

undertaken with the following parameters:-


• Wheel flat size of 25mm;

• Constant gross vehicle mass; and

• Varying suspension characteristics of damping and stiffness.

The graphs in Figure 6.4 (a) and (b) show how the effects of varying damping

characteristics influence the magnitude of the impact force for the empty and full

scenarios.

Impact Force vs Varying Damping(Empty 106t Narrow Gauge Heavy Haul Wagon


0.000

50.000

100.000

150.000

200.000

250.000

-40% -30% -20% -10% 0% 10% 20% 30% 40%

Varying Damping

Impa

ct F

orce

(kN

)

Figure 6.4 (a) Empty Wagons – Narrow Gauge with Varying Vehicle Damping

116

Impact Force vs Varying Damping(Full106t Narrow Gauge Heavy Haul Wagon


0.000

50.000

100.000

150.000

200.000

250.000

-40% -30% -20% -10% 0% 10% 20% 30% 40%

Varying Damping

Impa

ct F

orce

(kN

)

Figure 6.4 (b) Full Wagons – Narrow Gauge with Varying Vehicle Damping

The graphs in Figure 6.4 show that for both empty and full wagon scenarios, the

impact force magnitudes has no clear relationship to damping properties. However,

the effects of varying vehicle damping characteristics may have consequences for the

forces within the rollingstock which will not be investigated in this thesis.

The graphs in Figure 6.5 (a) and (b) show effects of varying suspension stiffness on

the magnitudes of impact force for empty and full wagons.

117

Impact Force vs Varying Spring Stiffness(Empty 106t Narrow Gauge Heavy Haul Wagon


0.00

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200.00

250.00

-40% -30% -20% -10% 0% 10% 20% 30% 40%

Varying Spring Stiffness

Impa

ct F

orce

(kN

)

Figure 6.5 (a) Empty Wagons – Narrow Gauge with Varying Suspension Stiffness

Impact Force vs Varying Spring Stiffness

(Full 106t Narrow Gauge Heavy Haul Wagon @ 80km/hr with 25mm Wheel Flat)

0.00

50.00

100.00

150.00

200.00

250.00

-40% -30% -20% -10% 0% 10% 20% 30% 40%

Varying Spring Stiffness

Impa

ct F

orce

(kN

)

Figure 6.5 (b) Full Wagons – Narrow Gauge with Varying Suspension Stiffness

Figures 6.5 (a) and (b) show that varying effects of varying suspension stiffness also

had very little effect on the impact forces and hence too little to include as part of a

probabilistic analysis of impact forces.

118

6.4.4 Varying Wheel Maintenance Practices

From Chapter 5, wheel maintenance practices seem to have a significant influence on

the magnitudes of impact forces as seen in the graphs of Figure 5.6 and 5.8 where the

impact forces of Braeside and Raglan were compared.

To quantify the effects of varying wheel maintenance practices on impact force

magnitudes, DTRACK simulations were undertaken using the following parameters:-


• Constant gross vehicle mass;

• Constant suspension characteristics; and

• Varying wheel flat sizes of 5, 25, 50, 75 and 100mm.

The graphs in Figure 6.6 (a) and (b) show the effect of various wheel flat sizes on the

magnitudes of impact forces for the empty and full heavy haul wagons.

Wheel Flat Sizes Vs Impact Force(Empty 106t Narrow Gauge Heavy Haul Wagon @ 80km/hr)

0

100

200

300

400

500

600

700

800

900

1000

0 10 20 30 40 50 60 70 80 90 100 110

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Impa

ct F

orce

(kN

)

Figure 6.6 (a) Empty Wagons – Effect of Wheel Flat Sizes on Impact Force

119

Wheel Flat Size Vs Impact Force(Full 106t Narrow Gauge Heavy Haul Wagon @ 80km/hr)

0

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700

800

900

1000

0 10 20 30 40 50 60 70 80 90 100 110

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Impa

ct F

orce

(kN

)

Figure 6.6 (b) Full Wagons – Effect of Wheel Flat Sizes on Impact Force

From the two graphs in Figure 6.6 (a) and (b) it is evident that wheel flat size is

almost linearly related to impact forces for both empty and full wagons.

Figure 6.6 shows that a more lenient wheel maintenance practice would lead to a

significant increase in impact forces, this is as wheel flat sizes increase. In addition,

a more lenient wheel maintenance practice would reduce the lifespan of the railway

tracks components as the return periods for given large impacts would decrease.

120

6.5 Consequences of Varying Parameters

Varying speed and wheel maintenance practice have been shown to have a

significant influence on the magnitudes of impact forces generated by wheel defects.

These changes in operations will also have considerable implications on the

probability and return periods determination of railway track that were presented in

Section 6.3.

The graphs in Figure 6.7 (a) and (b) show the consequences of changes to the

operational speeds (assuming no change in maintenance practice) on the impact force

distributions that were presented in Chapter 5 for the Braeside and Raglan sites

respectively.

Changes to Impact Forces Vs No. of Axles Due to Changes in Operational SpeedHeavy Haul Braeside

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The lines on the two graphs in Figure 6.7 show that if the operational speeds were to

be increased and the maintenance practice was to remain the same, there would be a

significant change in the impact force distributions.

For example, using the Braeside data in Figure 6.7 (a) for the current operating speed

of 80km/hr an expected impact force of 310kN would occur at least once in a one

year period. However if the operating speed was to increase to 100km/hr, then for

the same one year period, the expected impact force would increase to 440kN.

The return period for a given impact force would also be changed if the operational

speeds at Braeside and Raglan were to change. The two graphs in Figure 6.8 (a) and

(b) quantify the effects of operational speeds on the impact force return periods for

Braeside and Raglan.

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From the graphs in Figure 6.8, the effect of speed has considerably altered the return

periods of the impact force. For example, using the graph in Figure 6.8 (b) for a

return period of 50 years at the current operating speed (80km/hr) an impact force of

approximately 400kN can be expected. However if the operational speeds were to be

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lifted to 100km/hr than the expected impact force for a 50 year return period would

increase to 560kN.

This has major implications for railways as operational speeds are likely to increase

in the future. Therefore if railway components are to have a long design life,

considerations for increased magnitudes of impact forces due to increased

operational speeds must be considered.

A correlation between the impact force magnitude, speed and wheel flat size for

empty and full wagons can be made from the data collected by the WCM and the

DTRACK simulations. The graphs in Figure 6.9 show the relationship between the

three parameters for the empty and full wagon scenario.

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Figure 6.9 (a) Impact Force VS Speed VS Wheel Flat Size (Empty)

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Figure 6.9 (b) Impact Force VS Speed VS Wheel Flat Size (Full)

As many railway operators’ wheel maintenance standards in Australia specify a

maximum allowable wheel flat size rather than a maximum allowable impact force,

the graphs in Figure 6.9 can be used to correlate an impact force against the speed of

the vehicle and wheel flat size. For example, the graph in Figure 6.9 can be used in

conjunction with the graph in Figure 6.7 so the maximum impact force can be

correlated back to a wheel flat size which can be used to compare against the current

wheel maintenance standards at Braeside and Raglan.

In the development of limit state design, the ‘worst case scenario’ is of greatest

interest to the designer. Therefore the full wagon scenario in Figure 6.9 (b) would be

used in the development of limit state methodology rather than the empty wagon

scenario where the impact forces are significantly lower.

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6.6 Summary

The wheel impact force distribution graphs presented in Chapter 6 will be used in

Chapter 7 to establish the return periods and risk analysis for the development of

limit state design for railway track. The impact force distribution graphs presented

were based on data for a unique scenario that is not necessarily representative of all

railway traffic operating. However the methodology presented is expected to be

applied to other operating conditions.

For the development of a limit state design methodology for railway track,

allowances could be needed for the effects of varying parameters such as changes to

operating speeds, vehicle unsprung mass, vehicle suspension characteristics and the

wheel maintenance practices, as these parameters can greatly affect the magnitudes

of impact forces experienced in railway track. In Chapter 6 changes to these

operating parameters were investigated using the DTRACK model.

The DTRACK simulations found that impact forces were affected to any significant

extent only by changes to the parameters of speed and maintenance practices. The

effect of unsprung mass and suspension characteristics were negligible.

The wheel impact force distributions graphs in Figure 6.7 and 6.8 have shown that

increasing operational speed (if the maintenance standards were to remain the same)

would change the magnitude of impact force and return period predictions.

Therefore if the operational speeds were to be increased, the maintenance standards

would have to become more stringent to compensate, because changes to the

maintenance standards also had a significant effect on the magnitudes of impact

forces as seen in the graphs in Figure 6.9.

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In addition to the consequences for design loadings, changes to operational

conditions would also have an impact on the business functions of a railway.

Consequences to the business operations may include items such as track access

charges, wheel maintenance standards and cost of track construction and

maintenance. The consequences to business due to the operational changes would be

further explained in the following chapter.

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CHAPTER 7

Implications for Limit State Design of Railway Track

7.1 Introduction

The current railway standards in Australia such as the Prestressed Concrete Sleeper

Code (AS1085.14, 2003) are still based on outdated allowable stress principles as

mentioned previously in Chapter 2. Allowable stress principles have a number of

limitations such as ignoring ultimate element strengths, probability of failure and of

loads occurring and the risks associated with the structure.

Such limitations could lead to the over design of structures and hence uneconomical

design of the structures. Because of these limitations structural designs based on

allowable stress principles have become increasingly inadequate and the need has

arisen to design structures based on probability and material strengths (Allen, 1982).

This chapter will provide a background on limit state design and examine how the

wheel impact force distribution graphs shown in Chapter 6 can be used to establish a

limit state design methodology for railway track. As the Rail CRC project 5/23 was

primarily focused on the ‘Dynamic Analysis of Track and the Assessment of its

Capacity with Particular Reference to Concrete Sleepers’, this chapter will focus

primarily on a limit state design for a railway concrete sleeper.

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7.2 Background on Limit State Design

7.2.1 Limit State Concepts

Allowable stress design examines the capacity of a structure by calculating the

elastic stresses in it due to the maximum expected loads, and comparing them with

allowable stresses. The allowable stress is equal to the failure stress of the material

divided by a safety factor. Safety factors for new materials were estimated in

comparison with those for traditional materials by taking into account the nature of

failure for the new material and its uncertainty or variability (Allen, 1982).

Allowable stress design provides the following inequality for design.

FSFf kk <

Where fk = Stress due to applied loads

Fk = Limit stress

FS = Factor of safety which depends on characteristic of failure of the

material

Allowable stress design has a number of limitations such as ignoring ultimate

material strengths, probability of failure and probability of loads occurring and the

risks associated with the structure. These limitations may lead to the design of

structures with larger or stronger elements and hence an uneconomical design of

structures. Because of these limitations, allowable stress design methodology

became increasingly inadequate and the need to design structures based on

probability and material strengths is needed (Allen, 1982).

Limit state theory introduces the concepts of partial safety factors, characteristic

loads and characteristic strengths based on a probability approach. It provides the

framework which will enable design to be more accurate and therefore more

economical. Limit state theory also enables research results to be incorporated into

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standards as they become available, and guides further research by focusing attention

on deficiencies in knowledge (Hughes, 1980).

7.2.2 Limit State Methodology

The basis of the limit state approach is the application of statistics to establish the

probabilities of extreme load events occurring and then developing load factors as

margins of safety. Therefore, limit state design allows for a more logical design

process than implicit allowable stress design. The adoption of limit states also

permits the designer to vary the load factors for design and thus improving overall

economy of the design (Campbell and Allen, 1977).

The development of limit state approach has essentially five components

(Ellingwood and Galambos, 1982):

a. Develop statistical data to describe the basic load and resistance variables.

b. Establish procedures for calculating reliabilities of structural members and

systems. Ideally, the performance criteria should be based on a system

reliability requirement. However, current practice usually checks

performance on the basis of individual member behaviour.

c. Establish target reliabilities for design by analysing reliabilities associated

with structural members designed according to existing criteria. This

enables the probability based criteria to be related to existing acceptable

practice and provides the continuity that is necessary from one design

specification to the next.

d. Select a deterministic format that balances theoretical appeal with the need

for simple safety and serviceability checking procedures in professional

practice. Determine general load criteria suitable for all construction

materials.

e. Develop resistance criteria that are consistent with the load criteria selected

in step 4 such that reliabilities are close to the target values selected in step

3.

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The calculation of limit state factors requires the probability distribution of each load

and resistance variable. The probability distribution must also characterise the

uncertainty in the variables that would be expected in structures in service. The limit

state factors must also allow for errors that may be in the data collection process such

as limited measurements, errors in modelling and variability in data etc.

In order to simplify and unify the present prestressed concrete sleeper code

(AS1085.14, 2003) it is suggested that the following principle be adopted. The

resistances, resistance factors and structural theory depend only on the material and

type of structure and will therefore be contained in material design aspects (for

example, expand the current concrete code AS3600 (2001) to accommodate railway

sleepers). The loads, load factors and main serviceability requirements depend only

on the use of the structure and will therefore be given in a new railway loading code

(for example, make AS1085.14 into a railway loading code).

All structures have two basic requirements in common: safety from collapse and

satisfactory performance of the structure for its intended use (Allen, 1982). Limit

states define the conditions in which a structure fails to satisfy these basic

requirements. A limit state condition is a condition where a structure or structural

element in some way becomes unfit for its intended purpose (Ellingwood et al.,

1982).

Limit state conditions are generally classified into three major categories:

(a) Ultimate Limit State, transformation of the structure into a mechanism that is

no longer capable of supporting the applied load without collapse or

excessive deformation.

(b) Serviceability Limit State, deformations, which affect the efficient use or

appearance of structural or non structural elements.

(c) Other considerations or limit states: fatigue, durability, fire resistance,

lightning etc.

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For each limit state condition, the loads are multiplied by a load factor which takes

into account the probability of deviations of the load. When combinations of loads

are considered, the load factor may be decreased to take into account the reduced

probability of different loads acting simultaneously. The load factor may also be

adjusted by an importance factor to increase or decrease the safety of the structure

depending on the severity of the consequences. The limit state inequality can be

expressed as:

uRS φ≤*

Where S* = design action effect due to the factored design loads.

φRu= factored design capacity of a member

In the design of limit states the structural resistance, R and load effects, S are

represented in a reliability model and the relationship between the two parameters

are illustrated in Figure 7.1.

Figure 7.1 Probability density functions of load and strengths (Campbell and Allen,

1977)

From the graph, there is clearly a degree of safety if the load applied does not exceed

the component’s maximum sustainable load. The degree of safety is defined as the

safety factor, n and is usually expressed as;

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n = Maximum Load/Actual Load

And if n = 1 than the component is on the point of failure

n < 1 then the component is in a failed state

n > 1 then the component is safe

Figure 7.2 provides the variations of safety factor with respect to load effects and

structural resistance. Generally the larger the safety factor, the less economical the

structure will be as the structure may be designed for loads that may never be

encountered in its service life.

Figure 7.2 Variations in probability functions with varying safety factors (Wright,

2000)

The benefits of variations in probability functions are that if the consequences of

failure are unacceptable or expensive, the designer is entitled to increase the safety

factor or increasing the strength of the component. Alternatively, if the consequence

of failure more acceptable and the expense is minimal, the designer may reduce the

safety factor to enable a more economic design as in temporary structures. Another

benefit of the reliability model is that it allows for combination of loads to be treated

as a single load effect and analysed using the same methodology.

The advantage of a limit state approach is that it allows the designer to define the

limiting state for individual or combination loads based on characteristic probability

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distributions and applications of those loads. The designer also has the option select

the material strengths and other properties based on the degree of reliability that the

design requires.

The advantages of limit state concepts can be adapted to railway track engineering

such as developing a concrete sleeper based on the probability of forces that the

sleeper may encounter during service life. Limit state concepts also presents possible

cost savings through the ability to run higher speeds and axle loads on existing

infrastructure without wholesale changes. The adaptation of limit state concepts to

railway track engineering has the potential to develop a more economic railway

based on probabilistic loads.

7.2.3 Material Resistance

At present, Australian Standards have the Concrete Structures Code (AS3600, 2001)

for reinforced and prestressed concrete structures. The principal objective of the

code is to provide designers with nationally acceptable unified rules for the design

and detailing of concrete structures and elements, with or without steel reinforcement

or prestressing tendons, based on the principles of structural engineering mechanics.

The secondary objective is to provide performance criteria against which the finished

structure can be assessed for compliance with the relevant design requirements

(AS3600, 2001).

AS3600 (2001) is a fairly comprehensive code that covers the reliability of concrete

structures under different loading conditions and environments. The principles for

the design of concrete sleepers are similar to any structural concrete design.

However the current AS3600 (2001) does not cover high frequency and sudden

dynamic impact loads that are experienced by prestressed concrete sleepers.

Experimentation by Pandrol Australia (1987) examining the effect of rail pads to the

dynamic loads induced into prestressed concrete sleepers in Queensland Railways

found that the failure of some concrete sleepers has been caused by excessive

flexural vibration. The Pandrol report also found that the properties (such as elastic

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modulus and stiffness) of the rail pad also influence the dynamic responses of the

sleeper. Therefore it is important to understand the effects the properties of the rail

pad has on concrete sleeper loadings.

The responses of prestressed concrete sleepers are currently being researched under

the Rail CRC Project 5/23 at the University of Wollongong. The main aim of the

research at the University of Wollongong is to develop a better understanding of the

dynamic responses of concrete sleepers. The project will also investigate the

different responses concrete sleepers have to different rail pad properties. The

proposed research will be working in conjunction with the University of Wollongong

to develop limit state factors for recommendation to Standards Australia.

Before limit state factors can be developed for prestressed concrete sleepers, two key

issues must be understood. The first is the understanding of the responses concrete

sleepers have to high frequency loadings that are caused by railway traffic. The

second is establishing the probability of significant forces due irregularities at the

wheel/rail contact face (described in the following chapters).

7.2.4 Load Effects

Most structural loads may be thought of as consisting of a basic load parameter

which is essentially independent of the structure; a modelling parameter that

transforms the spatially and temporally varying load into an equivalent uniform (and

usually static) load for analysis and design purposes, and finally, an influence

coefficient or analysis factor that transforms the uniform load into a structural action

such as a beam moment (Ellingwood et al., 1982).

The loading parameter for railway track is extremely complicated compared to

structural loads, due to the dynamic behaviour of the loads and the impact loading

due to defects at the wheel/rail interface. Studies undertaken in Japan by Wakui and

Okuda (1999) have classified the effects of railway loading into two categories to

simplify load effects induced into railway track;

Quasi-static wheel loading – forces generated by vibrations of the vehicles

unsprung mass

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Impact wheel loading – forces generated by collisions between the vehicles

unsprung mass and the railway track.

By categorising the loading effects, limit state factors can be derived for each

condition and the effects analysed separately. The study undertaken by Wakui and

Okuda (1999) was mainly concerned with the loading effects at the rail seat. The

forces were measured at the rail seat because designers can design the concrete

sleeper and the subgrade to transmit these forces.

However, the study does not develop a methodology for limit state factor

development or provide any statistical data for wheel flat defects experienced in

Japan and only presents the effects of various wheel flats at various speeds on rail

seat loadings.

The adoption of limit state principles into standards is not a new concept as most

structural codes in Australia and internationally has incorporated these principles.

However, this literature review has found that railway design standards in Australia

and internationally are not based on limit state principles.

Wakui and Okuda (1999) have identified that the primary hindrance to developing

the standards from conventional allowable stress principles to contemporary limit

state principles has been the understanding of the complex dynamic characteristic of

railway track. The defects at the wheel/rail interface have been identified as the main

cause by many researchers such as Tunna (1988), Jenkins et al. (1974) and Frederick

(1978) that generate the largest dynamic forces and will be examined in the

following chapter.

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7.3 Definition of a ‘Failed’ Concrete Sleeper and Limit State

Conditions

In the development of a limit state design methodology for concrete sleepers, a clear

definition of concrete sleeper failure must first be established as the design limit state

conditions are heavily dependent on the definition of failure; i.e. what limiting

conditions can be defined that relate to the operations of a railway system.

In structural engineering, the definition of failure is when the structural element is

unable to sustain the bending or shear or axial actions imposed onto the structure.

However, this definition of failure cannot be applied to railways as the functions of a

concrete sleeper are different from the functions of a structural element in a building.

In Australia, railway organisation will condemn a sleeper when its ability to hold top

of line or gauge is lost. These two failure conditions are caused by the following

conditions:-

a) Abrasion at the bottom of the sleeper causing loss of top;

b) Abrasion at the rail seat causing a loss of top;

c) Severe cracks at the rail seat causing the ‘anchor’ of the fastening system

to move and spread the gauge;

d) Severe cracks at the midspan of the sleeper causing the sleeper to ‘flex’

and spread the gauge; and

e) Severe degradation of the concrete sleeper due to alkali aggregate

reaction.

It should be noted that abrasion and alkali aggregate reaction are due to the failure of

the concrete material and not structural failure of the sleeper as a structural element.

However, material factors such as abrasion and chemical resistance could be

included in a revised version of the Prestressed Concrete Code (AS1084.14, 2003) in

a manner similar to that adopted by the Concrete Structures Code (AS3600, 2001).

137

The definition of the structural failure of a concrete sleeper should therefore be based

on the concrete sleepers’ inability to hold top of line and gauge caused by the loading

environment in which the sleeper is exposed to.

It should also be noted that unlike the traditional structural engineering situation

where the failure of a single element can lead to catastrophic collapse of a major part

of a structure, the failure of a single sleeper does not constitute a failed track

structure or failure of the systems ability to carry railway traffic at full capacity.

Therefore, the number and arrangement of failed sleepers in a given cluster must be

given consideration as certain clusters of failed sleepers would constitute a failed

track structure.

The number and arrangement of sleeper cluster failure should be determined by the

track asset owners as each organisation in Australia has their own separate sleeper

cluster standards. The determination of the probability of sleeper clusters failing can

be calculated through a series of Monte-Carlo simulations and the level of risk

associated with cluster failure can also be established. However, these simulations

and risk assessments will not be presented in this thesis as it falls outside the scope of

the project.

The concept of limit states design defines a condition when the element or system

ceases to fulfil its design constraints and is no longer fit-for-purpose. Such a

situation is deemed to be ‘failure’ of the element or system. The outcome of this

failure can be replacement or repair of the element or system, depending upon which

design constraint has been exceeded, i.e. depending upon the type of failure

condition. For example, if a concrete beam in a building exceeds the ultimate design

limit state, replacement is usually the only option. However if the beam has only

exceeded the serviceability limit state (such as excessive cracking) than repair may

be all that is required.

For railway concrete sleepers, the design limit state conditions would have to be

slightly different to the traditional structural approach and should take into

consideration the railway track’s ability to continue to operating. Proposed below

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are three limit state conditions that are believed to encompass the design limit states

relevant to the design of railway concrete sleepers.

1. Ultimate Limit State

A single once off event (for example a severe wheel flat or dipped joint) that

generates enough force to fail a single concrete sleeper due to that one event. Failure

under such a severe event is expected to fit within failure definitions (c) and (d)

above.

2. Damageability Limit State

A single concrete sleeper that has accumulated damage progressively over a period

of years to a point where it is considered to have reached failure. Such failure could

come about from excessive accumulated abrasion or from cracking having grown

progressively more severe under repeated loading impact forces over its lifetime.

Failure due to accumulated damage fits within failure definitions (a) to (e).

3. Serviceability Limit State

This limit state defines a condition where sleeper failure is beginning to impose some

restrictions on the operational capacity of the track. The failure of a single sleeper is

rarely if ever a cause of a speed restriction or a line closure. However, when there is

a failure of a cluster of sleepers, an operational restriction is usually applied until the

problem is rectified.

The proposed limit state conditions above follow a similar format to that in the

current structural standards in the Standards Australia series. The limiting conditions

proposed would form the basis for designing a concrete sleeper based on both

engineering and commercial risks and hence a more realistic design outcome for

railway track can be achieved.

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7.4 Formulation for the Calculation of Design Wheel Load

The proposed methodology for the calculation of the design wheel load is similar to

the methodology that is found in the current structural standards (such as Loading

Code AS1170, 2002) found in Standards Australia, where the design load (P*) is

determined by a series of load combinations and load factors that are based on the

probability and return periods of loads occurring. In structural engineering (AS1170

Clause 4.2.1, 2002), one of the design actions (P*) is determined by:-

P* = 1.2G + 1.5Q Equation 7.1

Where, G = Permanent Load Q = Live Load

Equation 7.1 can be modified to suit the design of railway loadings where the design

action load (P*) can be transformed into a design wheel/rail force (F*). Then

Equation 7.1 would become:-

F* = 1.2 Fs + Fi Equation 7.2

Where, Fs = Static wheel load Fi = Wheel/rail impact force

The calculation of the static wheel load (Fs) would be based on the maximum

allowable load limit set by the rail track owner. For the case of Braeside and Raglan,

the maximum allowable load limit was 28.5tonnes/axle which would be used in those

cases for the static wheel load value (Fs). The 1.2 factor for the static axle load is to

allow for the overloading of the vehicles and is based on the Braeside and Raglan

graphs in Figure 5.14 where a number of vehicles were loaded up to 20% above the

target axle load.

The calculation for the wheel/rail impact force (Fi) would be determined by either the

defects at the wheel or rail interface and not both as the resultant impact force of

these two events are independent of one another. For example, a wheel flat

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impacting on a dipped joint is assumed to result in the same magnitude of impact

force from a normal round wheel impacting on a dipped joint.

Therefore, there needs to be two methods for determining the design wheel/rail force

(Fi) where it can either be:-

Fi,w = Design impact force caused by wheel defects; or

Fi,r = Design impact force caused by rail defects.

The two design wheel rail forces above will form the basis for developing the design

wheel/rail force for railway track. The calculation of the design wheel force caused

by the wheel (Fi,w) or rail (Fi,r) would be determined by a series of factors that are be

based on the probability of occurrence of wheel or rail interface defects within the

lifetime of the sleeper.

The wheel/rail impact force caused by rail defects (Fi,r), such as dipped joints, will

not be investigated in this thesis due to time constraints and because the data required

would fall outside the scope of the research project.

The use of factors in the calculation of design forces is not a new concept. The

current Australian Standards structural series such as the Loading Code (AS1170,

2002) already contain loading factors within the standards which are based on the

probability and return periods of extreme events occurring. For example, the

calculation of design wind gust speed (AS1170.2 Clause 3.2.2, 2002) is calculated

by:-

dtscatzRsit MMMMVV ),(, =β Equation 7.3 Where, Vsit,β = Design site wind speed VR = Gust wind speed M(z,cat)= Terrain category factor Ms = Wind shield factor Mt = Topographic factor Md = Wind direction factor

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The factors (M(z,cat), Ms, Mt, Md) used for the calculation of the design wind speed are

determined by the parameters (such as structure location and direction) that may

increase or decrease the design wind speed of the structure. The derivation of these

factors is based on comprehensive wind speed data from which probabilities and

return periods can be determined.

When the designer has determined the design wind speed presented in Equation 7.3,

the designer would then be able to calculate the actions on the structural elements via

a computer assisted design program and design the structure accordingly.

The calculation of the design wheel/rail force can be analogous to the calculation of

the design wind speed for structural engineering where design wheel/rail force can be

determined by a series of factors based on probability and return periods drawn from

a comprehensive set of data. Similar to structural engineering, once the design

wheel/rail force has been determined, the designer may then utilise a computer

assisted design program such as DTRACK to calculate the actions on the railway

track components and design those elements accordingly.

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Methodology for the Determination of the Design Wheel/Rail Force Due to Wheel Defects (Ft,w)

The proposed calculation of the design wheel/rail force due to wheel defects (Fw*) is

proposed to be calculated by the following equation:-

wiswt FFF ,),( 2.1 += Equation 7.4

The determination of the static wheel force (Fs) was mentioned earlier and is based

on the maximum allowable axle load specified by the railway track owner. The

impact force caused by wheel defects (Fi,w) is calculated by the following equation:-

IFFIwmimpwi kkkFF =, Equation 7.5

Where, Fimp = Impact force caused by a wheel defect kwm = Wheel maintenance factor kI = Track importance factor kIFF = Impact force factor

The calculation of the impact force caused by a wheel defect (Fimp) could be based on

the maximum allowable wheel impact force specified by the railway track owner and

would be the ‘base case’ for the design of railway track forces. For example, the

code of practice for the defined interstate network in Australia (ARA, 2003), the

maximum allowable P2 force allowed to be induced into the track is 230kN for

freight vehicles, therefore for this case, the impact force (Fimp) would be 230kN.

The determination of the factors (kwm, kI and kIFF) introduced in Equation 7.5 are

analogous to the calculation of the design wind speed in Equation 7.3 where the

factors are based on return periods and probabilities of occurrences.

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Wheel Maintenance Factor (kwm)

The proposed wheel maintenance factor (kwm) is based on the frequency of wheel

maintenance and the level of wheel maintenance standards used by the railway

operator. The wheel maintenance factor allows for the variability in wheel

maintenance intervention strategies and maintenance standards adopted by different

operators. For example, in a scenario such as the interstate railway network where

there are numerous rail operators with various wheel maintenance standards and

strategies, the wheel maintenance factor (kwm) can cater for those various standards

and strategies to minimise the probability of failure for the railway track.

Table 7.1 shows three wheel maintenance factors that are proposed which would be

based on the frequency of intervention, intervention strategy and standards applied to

the maintenance of rollingstock wheels.

Table 7.1 Proposed Wheel Maintenance Factors

Wheel Maintenance

Group

Wheel Maintenance Characteristics Wheel Maintenance Factor (kwm)

Group 1 A very high standard of wheel maintenance in which wheel tread defects are detected and removed quickly. The maximum wheel tread defects tend to be small.

0.9

Group 2 This group includes railway lines that operate an impact detector to assist the operator to identify and rectify the wheel defects. The wheel maintenance standard in this group does not represent the highest achievable wheel maintenance standard; rather, the early detection of wheel defects reduces the risks of further damage to the railway track. The mineral wagons traversing Braeside and Raglan would fit into this category.

1

Group 3 This group would include situations where vehicles are subjected only to visual inspection and detection by drivers.

> 1

The wheel impact maintenance factors (kwm) in Table 7.1 would help determine level

of risk a railway track owner is willing to accept from the railway operator. The

determination of the wheel maintenance factor should be based on risk assessments

carried out by the railway track owner and operator.

144

Track Importance Factor (kI)

The proposed track importance factor (kI) is based on the level of acceptable

commercial risk (such as delays to operations) the railway track owner is willing to

accept. For example, in a scenario where failed sleepers affect the operation of a

profitable heavy haul railway carrying high traffic volumes, the consequences would

normally be much greater and of more concern to the track owner and train operators

than the same failure of sleepers on a branch line with low traffic volumes.

Therefore, sleepers installed on a heavy haul line should be designed with a lower

risk of failure than sleepers on a branch line.

The track importance factor (kI) should not attempt to cover extreme events such as

derailments as these events arise from exceptional circumstances and it would be

impractical and uneconomical to design sleepers based on such events. Therefore,

the ‘cost’ for the loss of human life for example should not be considered in

determining the track importance factor (kI).

Commercial risks associated with changes to operational conditions such as

operational speeds are not part of the track importance factor (kI). These matters

would be taken into account by the impact force factor (kIFF) presented in the

following sections.

Table 7.2 presents possible values for the track importance factor (kI) and the

categories of railway track associated with the factor. The factors are ranked

according to importance, where 1 is the greatest importance and 4 is the least

importance.

The track importance categories proposed are a reflection of the typical range of

railway track that is found in Australia. The track importance factors should also be

able to be applies to other railway operations such as passenger, cane tram operations

or light rail.

145

Table 7.2 Possible Track Importance Factor Values

Track Importance

Category

Line Characteristics Possible Track

Importance Factor (kI)

Category 1 Lines which meet the one or more of the following criteria:- a) The sole or critically important source of revenue

to the business; b) Consumes a major portion of the organisations

maintenance budget; c) Relatively short delays to services can have a

significant impact on reputation, profitability and/or service obligations;

d) Traffic is overwhelmingly of a single type of key importance to the business; and

e) Restricted track maintenance windows. Examples would be heavy haul or inner city suburban lines.

1

Category 2 Lines which meet some of the following criteria:- a) Carry mixed traffic producing a significant

income to business; b) Consume a large part of the maintenance budget; c) Short delays can be tolerated with limited effect

on reputation, profitability and/or service obligations; and

d) Regular track maintenance windows available. Examples would be lines such as freight connections between major city centres.

0.95

Category 3 Lines which meet most of the following criteria:- a) Carry routine mixed traffic contributing to

business revenue; b) Receives regular programmed routine

maintenance; c) Low to average volumes of traffic compared to

the business’s prime operations; and d) Long delays can influence reputation,

profitability and/or service obligations. Examples would be lines that carry freight connections between city centres, but are not as important as Category 2.

0.90

Category 4 Lines which meet all of the following criteria:- a) Marginal profitability; b) Infrequent or minor maintenance requirements; c) Delays to services are rarely of serious

consequence; and d) Irregular, infrequent traffic. Examples would be branch lines.

0.85

146

Impact Force Factor (kIFF)

The impact force factor (kIFF) is based on the return periods and consequences of

changing operational characteristics, in particular changing operational speeds. The

formulation of the impact force factor would be analogous to the return factors found

in the Australian Rainfall and Runoff (2005) publication, where calculation of the

quantity of rainfall is dependent on and determined by the return periods and design

life of the structure. In the case of railway loading calculations the impact force

magnitude would also be dependent on and determined by the return periods and

design life of the railway track.

To illustrate how the return periods and probability of occurrence can be applied, the

data from the Braeside and Raglan wheel impact detectors will be used to calculate

the wheel impact factors for railway loadings. It should be noted again that the data

for Braeside and Raglan is for a single unique railway operation; for a more

comprehensive set of factors, more data of various wheel impacts from around

Australia would be needed.

The graphs in Figure 7.3 (a) and (b) shows the impact factors for the Braeside and

Raglan sites respectively. The dotted lines within the graph represent the impact

force factor for a given return period and speed as indicated by the values on the left

hand side of the graph. The dots were calculated by ‘normalising’ the impact forces

against the impact force generated at 60km/hr, 1 year return period (see graphs in

Figure 6.8). For example:

The base case impact force at Braeside for a speed of 60km/hr, 1 year return period

was 230kN, so the impact force factor for the base case was:

Impact force factor for 60km/hr, 1 year return period = 230/230

= 1

147

For 80km/hr, 50 year return period at Braeside, the impact force was 400kN,

therefore the impact force factor was calculated by:-

Impact force factor for 80km/hr, 50 year return period = 400/230

= 1.74

Near the left side of the graph in Figure 7.3 (a) and (b) are the speeds for the

respective impact force factors for each of the dotted lines. The lines of best fit

within each graph were drawn in by the writer and are representative of the dots for

the 60km/hr and 120km/hr speeds. The line of best fit for the 90km/hr speed was an

interpolation between the two other lines of best fits.

Impact forces generated by wheel defects under 60km/hr were not considered in the

graphs in Figure 7.3 as the forces are too small to have any significant effect on the

design of track components in the current common situations.

Impact Force Factor (Braeside)

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

0 10 20 30 40 50 60 70 80 90 100 110


Impa

ct F

orce

Fac

tor

120km/hr

90km/hr

60km/hr

60km/hr

80km/hr

100km/hr

120km/hr

Lines of Best Fit

Lines of Best Fit

Lines of Best Fit

Figure 7.3 (a) Impact Force Factor for Braeside

148

Impact Force Factor (Raglan)

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

0 10 20 30 40 50 60 70 80 90 100 110


Impa

ct F

orce

Fac

tor

120km/hr

90km/hr

60km/hr

120km/hr

100km/hr

80km/hr

60km/hr

Lines of Best Fit

Lines of Best Fit

Lines of Best Fit

Figure 7.3 (b) Impact Force Factor for Raglan

From the lines of best fit within each of the graphs in Figure 7.1 (a) and (b), an

equation of the lines can be derived based on the return periods and operational

speeds for both the sites.

The impact force factor (kIFF) calculated from Figure 7.1 (a) and (b) would be:-

Braeside: 73.0029.000278.0 −+= vRkIFF Equation 7.6 (a)

Raglan: 56.0029.000178.0 −+= vRkIFF Equation 7.6 (b)

Where, R= Return period (years) v= Velocity

149

7.5 Case Study

Assuming that the current operating speed at Braeside was to be increased to

100km/hr from 80km/hr, would the current concrete sleepers be able to carry the new

loadings under the ultimate condition of 50 years?

The calculation of the wheel/rail force from wheel defects using Equation 7.4

requires determination of Fs and Fi,w. For the current traffic operating at Braeside,

the maximum allowable axle load (Fs) is 28.5tonnes. Therefore,

Fs= 28.5 x 9.81

= 280kN/axle

= 130kN/wheel

From Equation 7.5 the design wheel/rail impact force (Fi,w) caused by wheel defects

is:-

IFFIwmimpwi kkkFF =,

As mentioned previously the base case impact force (Fimp) can be based on the

railway track owners specified maximum allowable impact force. For this example,

the base case impact force was suggested in Section 7.3 to be 230kN.

The wheel maintenance factor (kwm) for this case study would be Group 2 in Table

7.1 whereas the track importance factor (ki) would be Category 1 in Table 7.2,

giving:

kwm= 1

ki = 1

The impact force factor (kIFF) would be determined from Equation 7.7 as follows:-

For the 80km/hr case For the 100km/hr case 39.080019.05000185.0 −×+×=IFFk 39.0100019.05000185.0 −×+×=IFFk

73.1=IFFk 31.2=IFFk

150

The design wheel/rail force (Ft,w) for both cases becomes: For the 80km/hr case For the 100km/hr case

wiswt FFF ,),( 2.1 += wiswt FFF ,),( 2.1 +=

IFFIwmimpswt kkkFFF += 2.1),( IFFIwmimpswt kkkFFF += 2.1),( 73.1112301302.1),( ×××+×=wtF 31.2112301302.1),( ×××+×=wtF

wheelkNF wt /554),( = wheelkNF wt /687),( =

Therefore, if the current operating conditions were to change from 80km/hr to

100km/hr, it would be expected that there would be approximately 20% increase in

wheel impact force over a 50 year design life.

Checking these calculations with the graph in Figure 6.8 (a) where for a 50 year

return period, the expected wheel impact force for operating speeds of 80km/hr and

100km/hr are 400kN and 565kN respectively;

For the 80km/hr case For the 100km/hr case kNFF swt 4002.1),( += kNFF swt 5652.1),( += 4001302.1),( +×=wtF 5651302.1),( +×=wtF

wheelkNF wt /568),( = wheelkNF wt /733),( =

The difference in wheel impact force between the design wheel load and the Braeside

data in Figure 6.8 (a) for the 80km/hr and 100km/hr case was 4% and 2%

respectively. Therefore the methodology developed in Section 7.3 only has a small

margin of error and is adequate for design.

From the impact force versus speed versus wheel flat size graph in Figure 6.9 (b), a

wheel flat size can be correlated to the calculated impact forces for the 80km/hr and

100km/hr case. Table 7.3 shows the corresponding wheel flat to the calculated

design wheel/rail force (Fi,w) for a 50 year design life.

Table 7.3 Proposed Track Importance factors Vehicle Speed Design Wheel/Rail Force Corresponding Wheel Flat

80km/hr 534kN/wheel 60mm 100km/hr 687kN/wheel 61mm

151

The corresponding wheel flats in Table 7.3 can now be used to calculate the bending

moments in a concrete sleeper using the DTRACK computer model. Assuming that

the parameters of the existing track conditions at Braeside are the same as the

conditions used in Benchmark II (refer to Chapter 5) and is summarised below in

Table 7.4

Table 7.4 Braeside Track Parameters

Component Description Rail AS 60kg/m

Gauge 1065mm Rail Pad HDPE

Sleeper Type M270 Pandrol Concrete Sleeper (refer to Drawing No201-05 in Appendix XX)

Sleeper Spacing 685mm Track Bed Type Medium Stiffness (Stiffness 50.1MN/m, Damping

159kNs/m)

The properties of a typical heavy haul wagon operating at Braeside are shown in

Table 7.5 below.

Table 7.5 Braeside Parameters Component Description

Gross Vehicle Mass 106 tonnes Axle Spacing 1780mm

Sideframe Mass 500kg Sideframe Mass Moment

of Inertia 500kg.m

Suspension Stiffness 2MN/m Suspension Damping 0kNs/m

Wheelset Mass 1400kg Wheel Radius 457.5mm

Wheel Profile Radius at Contact

360mm

The parameters in Table 7.3, 7.4 and 7.5 were entered into DTRACK to calculate the

bending moments of the rail seat (M*railseat) for the two speeds 80km/hr and

100km/hr. Table 7.6 shows the resulting bending moments.

Table 7.6 Rail Seat Bending Moment M* Vehicle Speed Design Wheel/Rail Force Rail Seat Bending Moment (M*)

80km/hr 554kN/wheel 38.1 kN.m 100km/hr 687kN/wheel 39.4 kN.m

152

From the Drawing No 201-05 found in Appendix XX, the ultimate bending moment

capacity (Mu) of the concrete sleeper that is found at the Braeside site is as follows

(calculation of the sleeper design capacity can be found in Appendix XX):-

Mu = 54.9kN.m

φMu = 0.85 x 54.9kN.m (φ value from AS3600 Clause 2.4.2, 2001)

= 47kN.m

*MM u >∴φ from Table 7.6

The calculated design sleeper bending moment at the rail seat (φMu) and the design

rail seat bending moment (M*) have shown that there is still approximately 15%

reserve of strength in the current concrete sleepers at Braeside based on a 50 year

return period.

The case study presented in this section assumes the concrete sleeper experiences

complete flexural failure at φMu which is the ultimate limit state prescribed in

Section 7.2.

The material reduction factor (φ) could possibly be increased for concrete sleepers

because they are manufactured in an environment where properties such as strength,

fabrication tolerances and quality of materials are well controlled. The stringent

quality control used to manufacture concrete sleepers increases the reliability of the

sleepers.

7.6 Implications for Railway Businesses

Transforming the current Standard Australia Prestressed Concrete Sleeper Code

(AS1084.14, 2003) from allowable stress to limit states methodology would have

implications for railway businesses. Limit states methodology would open up a

range of possibilities for railway businesses such as a more accurate evaluation of

capacity of the track asset, asset and risk management and more realistic track access

charges.

153

The accurate evaluation of the track asset would allow track asset owners to develop

better maintenance strategies such as replacement of the asset. Therefore, the track

asset owner may be able to increase the speeds and payloads of the rollingstock

based on a limit state evaluation of the track structure, without having to replace

hundreds of thousands of sleepers.

Using limit state principles for design and evaluation also allows track owners to

better manage the railway track asset based on probability and risk. For example, the

track owner may be able to better manage and determine the level and quality of

track maintenance based on the level of acceptable risk.

Limit state assessment of railway track also allows asset managers to calculate the

whole life of costing for the track asset with greater accuracy as probabilities of track

failure can be established. Limit state methodology would also better allow asset

managers to assess the longevity of the railway track components which could have

significant potential cost savings for the track owner. Risk management for railway

track will also become more accurate as the definition of track failure and its

consequences become clearer.

Track access charges can also be influenced as a result of limit state evaluation for

railway track. As risk management becomes more reliable, railway operators and

track asset owners would be able to negotiate access charges based on the level of

acceptable risks for both parties. For example, a rail operator would be able to

negotiate track access charges based on the level of their wheel maintenance

standards as the risks and consequences of failure can be estimated in terms of costs.

The implications of a limit state design is beneficial for business as it gives railway

operators and track owners’ flexibility to manipulate the design and standards of

railways based on their assessment of acceptable risks. The effect would be an

improved view of railways as a more viable means of transport.

154

7.7 Summary

This chapter has presented a limit state design methodology for railway loadings

based on the wheel/rail data presented in Chapter 5 and 6. The limit state design

methodology developed in this section is based on a single unique track and train

scenario; for a more comprehensive methodology, more wheel/rail data covering all

the various operational conditions in Australia would be needed.

Definitions of failure for railway track were also presented in this chapter based on

Australian railway organisations’ definition of sleeper failure, where the sleepers

ability to hold top of line or gauge is lost. Three limit state conditions were therefore

proposed which follow a similar format to those in the current Standards Australia

series.

A methodology for the calculation of the ultimate design wheel load was presented,

proposing a series of factors based on the probability of occurrence and the return

periods of wheel impact forces. The factors took into consideration the commercial

risks associated with track failure, reflecting the typical range of railway track found

in Australia.

A case study was presented in this chapter, which provided an example of how the

limit state methodology developed could be used to assess the capacity of the

existing concrete sleepers at Braeside. The case study found that under the current

Australian Standard for Concrete Structures design, the existing concrete sleepers at

Braeside do possess reserves of strength.

The development of limit states also has implications for railway businesses as it

provides a realistic evaluation of track performance, asset and risk management

become more reliable, possibly influencing determination of determination of track

access charges for operators. Limit state principles may also allow engineers to

design a more efficient railway track, leading to improvement of the profitability and

viability of railways as an alternative form of transport.

155

CHAPTER 8

Conclusions

8.1 Introduction

This thesis has presented a limit state design methodology for railway track for

recommendation to the Standards Australia next review of the Prestressed Concrete

Sleeper Code (AS1085.14, 2003). In the development of a limit state design

methodology for railway concrete sleepers, a comprehensive set of wheel/rail force

data for a unique railway operation has been collected and analysed to calculate the

probabilities and return periods of occurrences. The collected data was used in

conjunction with DTRACK to produce a limit state design methodology that can be

adapted to other railway operations and scenarios. The structure of this thesis had

five main parts:-

1. A review of the current Standards Australia and individual railway

organisations standards for railway track design, maintenance and operations;

2. The completion and benchmarking of the DTRACK model which will be

eventually become commercially available for the Australian railway

industry;

3. Presented a comprehensive set of wheel/rail impact force measurements that

was collected by the Teknis Wheel Condition Monitoring system;

4. Established a probabilistic analysis of the wheel/rail force distributions and

investigated the consequences of varying parameters to the impact force

distributions; and

156

5. The development of limit state factors and design methodology based on the

data collected at Braeside and Raglan.

8.2 Findings and Conclusions

The traditional methodology and current Australian Standards (AS1085.14, 2003)

used to design railway track is based on allowable stress principles. The design of

railway track based on allowable stress principles have a number of limitations

which include:-

- Neglect of material ultimate strengths;

- Ignoring the probability of failure and loads applied to the structure; and

- Ignoring the risks associated with failure of structural elements.

The limitations of allowable stress principles may lead to over-design and hence

uneconomical design of the railway track. Therefore there is a need to evolve the

current Concrete Sleeper Code (AS1085.14, 2003) from allowable stress principles

to a more realistic design philosophy.

Limit state principles allows for the design of the structural elements based on the

probability of occurrences, definitions of structural failure and risks associated with

failure. It provides a framework which will enable designs to be more accurate and

hence more economical. Limit state principles also allows designers to define the

failure conditions based on probability load distributions and design the structural

element based on the material strengths and degree of reliability required. Therefore

evolving the current Australian Standards Prestress Concrete Sleeper Code

(AS1085.14, 2003) from allowable stress principles to limit state principles has both

practicality and commercial benefits by reducing risks and improving productivity

for railway organisations.

As part of the goal in developing a limit state methodology for railway track, a

dynamic model was needed to investigate the complex interaction between vehicle

and track. The dynamic model chosen had to meet the criteria set by the Rail CRC

157

and had to demonstrate potential for further development. For these reasons the

Dynamic TRACK (DTRACK) model was identified as the best model for further

development and research.

The DTRACK model presented in this thesis is the completed revised version of the

DTRACK model presented by Steffens (2005). The original author of the program

(Cai, 1992) was contracted to correct the problems with DTRACK that were

identified by Steffens (2005). In addition a programmer had also been contracted to

finish the user friendly interface for DTRACK initiated by Steffens (2005).

Although the general structure of the DTRACK model had not changed, many of the

original features have been upgraded and improved. Improvements such as library

maintenance, data management and graphing abilities were included into the

upgraded DTRACK. The development of the DTRACK model was completed

within this thesis and is ready to be distributed to the Australian railway industry and

research community.

To test the reliability of the DTRACK model, a benchmarking exercise (known as

Benchmark II) was established where the results of DTRACK were benchmarked

against six other dynamic track models. In addition, Benchmark II compared the

results of all the participating models against field data which was collected at Lara

on the Melbourne to Geelong standard gauge railway track in Australia.

The results of Benchmark II found that the majority of the requested output

parameters of DTRACK was in good agreement with the results of the other

benchmarked models and the field data. However, Benchmark II also identified a

few issues with the outputs of the DTRACK model, where the magnitude of shear

force in the rail was too low and the bending moment of the sleeper contained many

peaks and dips in the magnitude which was not seen in the results of the other

models and field data.

The original author of the DTRACK model (Dr Zhenqi Cai) has been notified of the

problems and was addressing them at the time of writhing this thesis. The

uncharacteristic results of the DTRACK model did not affect the peak magnitudes of

158

the bending moment results and hence would not impede on the final outcomes of

this research.

In the establishment of a limit state methodology for railway track, a comprehensive

set of wheel/rail force data was needed. This thesis only examined the impact forces

caused by wheel defects for two main reasons:-

3. Wheel defects occur at random and have a high probability of occurring;

and

4. Impact events caused by wheel defects are not localised (such as dipped

joints) and can impact at random along a given section of railway track.

The wheel impact force data was collected by the Teknis wheel conditioning

monitoring system at Braeside and Raglan in Queensland. The data collected from

the WCM at Braeside and Raglan represented a single unique scenario (a heavy haul

line) and is not representative of the variety of railway operations in Australia.

However, the purpose of this thesis is to establish a limit state methodology that can

be adapted to other railway operational scenarios, therefore the data collected will be

used to establish a limit state design methodology for railway track.

The data collected from the Braeside and Raglan site showed that the impact force

distributions were heavily dependent on parameters such as:-

- Wheel maintenance practices and strategies;

- Vehicle speeds; and

- Driver behaviour of the vehicles.

The distribution of the impact forces that were presented formed the basis of

establishing a limit state design methodology for railway track. However, the data

collected from Braeside and Raglan only represents the current operating conditions

and does not consider future changes in operations such as speeds and wheel

maintenance practices which can greatly change the impact force distributions.

159

To predict the probabilities of occurrence and return periods of impact forces due to

future operating conditions, the DTRACK models was used to simulate the effects of

varying parameters such as operational speed, unsprung masses, suspension

characteristics and maintenance practices. The DTRACK simulations found that

speed and maintenance practices greatly affected the impact force magnitude and

will be used to investigate the changes to the impact force distributions of Braeside

and Raglan. The impact force distributions were recreated to accommodate the

changes of impact force magnitudes due to varying parameters.

The consequences of varying parameters dramatically changed the impact force

distributions and hence probability of occurrence and return periods. This has major

implications for the establishment of limit state design for railway track as the likely

changes in operational characteristics will significantly affect the magnitude of

impact forces and their distributions.

The concept of limit state design is dependent on a set of limit state conditions which

is based on the definition of structural failure. In the case of railways, the proposed

definition of concrete sleeper ‘failure’ is based on the sleepers’ inability to hold top

of line and gauge caused by the loading environment to which it is exposed to.

However, the failure of a single sleeper does not constitute the failure of the track

structure and does not compromise the railway tracks ability to carry traffic.

Therefore the limit state conditions should also consider the failure of sleeper

clusters based on each railway track owners own standards. The limit state

conditions proposed in this thesis are based on the economic consequences of failure

and should not be based on extreme conditions such as a derailment scenario.

The limit state methodology proposed in this thesis is analogous to the Australian

Standards Loading Code (AS1170, 2003) where the design load of a structural

element is determined by load factors and combinations based on the probability of

occurrence and return periods. This thesis presented a methodology for the

determination of the load factors based on the maintenance, track importance, return

periods and risks associated with failure, which is comparable to the calculation of

the design wind speed in Standards Australia Wind Code (AS1170.2, 2002).

160

A case study using the developed limit state methodology for railway track was also

presented in this thesis. The case study compared the capacity of the current

concrete sleepers used at Braeside and Raglan against the calculated capacity using

the presented limit state methodology.

The limit state design methodology for railway track also has implications for

railway businesses such as the asset management of the track asset, risk assessment

and track asset charges. The effects of limit state design for railway track would

improve the efficiency of track components and the feasibility of railways as a more

viable means of transport.

8.3 Recommendations

The DTRACK model and the limit state methodology presented in this thesis has the

potential to be further developed and improved. The following recommendations are

drawn from the conclusions of this thesis:

- The user friendly interface developed for DTRACK will require technical

support after commercialisation. Therefore a professional computer programmer

should be contracted to provide technical support for the DTRACK model via the

Rail CRC.

- From the Benchmark II exercise, it is evident that there are a few issues with the

outputs of the DTRACK program. Particularly in the shear force in the rail

where the forces were too small and the bending moment of the sleeper at the rail

seat and centre where there was an uncharacteristic bending moment profile that

was not found in the field results or other models outputs. The original author of

DTRACK should try to correct these uncharacteristic profiles before the

commercialisation of the program.

- The wheel/rail impact data was collected from Queensland Rails’ Braeside and

Raglan sites which represented a single unique heavy haul scenario which is not

representative of the variety of operations in Australia. Therefore more data

161

covering the variety of railway operations in Australia (such as freight and

passenger operations) would be needed for greater applicability of the limit state

methodology presented in this thesis.

- A more comprehensive collection of wheel/rail impact force data over a longer

period of time is needed to establish greater confidence in the presented

methodology.

- This thesis only covered the impact forces caused by defects at the wheel

interface. Therefore there is a need for further research on the impact forces

caused by the defects at the rail interface, particularly the probabilities and return

periods of rail defects.

- The developed limit state methodology presented in this thesis should be

recommended to the Australian Standards next review of the Prestressed

Concrete Sleeper Code (AS1085.14, 2003) when more comprehensive wheel/rail

data has been collected.

As the railway industry in Australia becomes more competitive with other modes of

transport, there will be an increasing commercial pressure to develop a more efficient

and reliable track structure. Evolving the current Australian Standards Prestressed

Concrete code (AS1085.14, 2003) may eventually lead to a more economic design of

the track structure which will consequently improve the viability of railways as an

alternative and competitive form of transport.

162

References

Allen, D., (1982), Limit States Design, [online] <http://irc.nrc-

cnrc.gc.ca/cbd/cbd221e.html> Accessed [13 April 2005].

AREMA. (1999), Manual for Railway Engineering, Landover, USA: AREMA.

ARTC (2002), Freight Vehicle Specific Interface Requirements, Australian Rail

Track Corporation - Engineering Standards. Sydney, NSW.

Australasian Railway Association (2002), Volume 5: Rollingstock (Draft), Part 2:

Common Requirements, Section 2: Commissioning and recommissioning, RCP-

2201: Performance Acceptance Requirements, Code of Practice for the Defined

Interstate Rail Network, Version 1, Australasian Railway Association.

Australasian Railway Association (2003), Volume 4: Track, Civil and Electrical

Infrastructure, Part 3: Infrastructure Guideline, Code of Practice for the Defined

Interstate Rail Network, Australasian Railway Association.

Broadley, J. R., G. D. Johnston and B. Pond, (1981), The Dynamic Factor, Railway

Engineering Conference, 7-10 September, Sydney.

Cai, Z. (1992), Modelling of Rail Track Dynamics and Wheel/Rail Interaction, PhD

Thesis, Queens University, Kingston, Canada.

Cai, Z. and G. P. Raymond, (1992), Theoretical model for dynamic wheel/rail and

track interaction., National Conference Publications, Institution of Engineers,

Australia, pp. 127-131.

Cai, Z. and G. P. Raymond, (1993), Responses of Railway Track to Dynamic and

Static Loading, International Heavy Haul Railway Conference, Beijing, China.

163

Cai, Z. and G. P. Raymond, (1994), Modelling the dynamic response of railway track

to wheel/rail impact loading, International Journal of Communication Systems, 2 (1),

pp. 95-112. New Jersey, USA.

Cai, Z., G. P. Raymond and J. O. Igwemezie, (1994), Contact loads from vertical

dynamic wheel/rail and track interaction, International Conference on Contact

Mechanics and Wear of Wheel/Rail Systems, Vancouver, Canada.

Campbell and Allen, (1977), Limit State Design and the Practising Engineer, ASCE

Seminar, 1 June 1977, Sydney, NSW.

CEN (2002), Railway Applications - Track - Concrete Sleepers and Bearers - Part 1

General Requirements, European Comittee for Standardization. Brussels, Belgium.

Dong, R. G. and S. Sankar, (1994), The Characteristics of Impact Loads due to

Wheel Tread Defects, ASME International Mechanical Engineering Congress and

Exposition, Chicago, USA.

Dong, R. G., S. Sankar and R. V. Dukkipati, (1994), A finite element model of

railway vehicle-track and its application to the wheel flat problem, Journal of Rail

and Rapid Transit, 208 pp. 61-72.

Eisenmann, J., (1972), Germans gain a better understanding of track structure,

Railway Gazette International, 128 (8), pp. 305-310.

Ellingwood, B. and T. Galambos, (1982), Probability Based Design Criteria for

Structural Design, Structural Safety, 1 pp. 15-26.

Ellingwood, B., T. Galambos, J. Macgregor and C. Cornell, (1982), A Probability

Based Load Criteria: Load Factors and Load Combinations, Journal of Structural

Engineering, 108 pp. 978-997.

Esveld, C. (2001), Modern Railway Track, Delft, Netherlands: Delft Univeristy of

Technology, MRT Productions.

164

Frederick, C. O., (1978), The effect of wheel and rail irregularities on the track,

Heavy Haul Railways Conference, September 1978, Perth, Australia.

Frederick, C. O. and D. J. Round, (1984), Vertical track loading, Track Technology:

Proceedings of a Conference, University of Nottingham, UK.

Grassie, S. L., (1992), Dynamic models of the track and their uses, FRA/ERRI

Conference, June 22-26, TU Delft, Netherlands.

Grassie, S. L., (1995), Benchmark Test for models of railway track and of

vehicle/track interaction at relatively high frequencies, Vehicle System Dynamics,

Supplement (24), pp. 355-362.

Grassie, S. L. (2003), Corrugation Analysis Trolley, Wedemark, Germany: Stuart

Grassie Engineering Solutions.

Hay, W. M. (1982), Railroad Engineering, New York, USA: John Wiley & Sons,

Inc.

Hughes, B. P. (1980), Limit State Theory for Reinforced Concrete Design, London,

UK: Pitman Publishing Inc.

Iwnicki, S. (1998), The Manchester Benchmark for rail vehicle simulation,

Manchester, UK: Manchester Metropolitan University.

Jeffs, T. and G. P. Tew. (1991), A Review of Track Design Procedures, Melbourne,

VIC: BHP Research - Melbourne Laboratories for Railways of Australia.

Jeffs, T. and G. P. Tew. (1992), A review of Track Design Procedures . Volume 2

Sleepers and Ballast, Melbourne, VIC: BHP Research - Melbourne Laboratories for

Railways of Australia.

165

Jenkins, H. H., J. E. Stephenson, G. A. Clayton, G. W. Morland and D. Lyon, (1974),

The effect of track and vehicle parameters on wheel/rail vertical dynamic forces,

Railway Engineering Journal, 3 (1), pp. 2-16.

Japanese Standard Association (1997), JIS-E1201 - Prestressed Concrete Sleepers -

Pretensioning Type, Japanese Standard Association.

Knothe, K., (1995), Benchmark test for models of railway track and of vehicle/track

interaction in the low frequency range, Vehicle System Dynamics, 24 (Suppl), pp.

363-379.

Knothe, K., R. Wille and B. Zastrau, (2001), Advanced Contact Mechanics - Road

and Rail, 17th IAVSD Symposium, Presentation notes, Copenhagen, Denmark.

Murray, M. H. and Z. Cai (1998), Prestressed concrete sleeper, Literature Review,

AS1085.14 Load Distribution & Impact Factors, Brisbane, QUT Report for:

Australasian Railway Association Inc.

Pandrol (1987), Measurement of Loads in Concrete Railway Sleepers in Service

Queensland Railways, Blacktown, NSW, Pandrol Australia.

Popp, K. and W. Schiehlen. (2003), System Dynamics and Long Term Behaviour of

Railway Vehicles, Track and Subgrade, Berlin, Germany: Springer-Verlag Berlin

Heidelberg.

Profillidis, V. A. (2000), Railway Engineering, London, England: Ashgate

Publishing Company.

QR (2001), Rollingstock Dynamic Performance, Safety Management System,

Version: 2, QR.

Shabana, A. A. and J. R. Sany, (2001), A survey of rail vehicle track simulations and

flexible multibody dynamics, Nonlinear Dynamics, Vol. 26 (No. 2), pp. 179-210.

166

Skerman, D., (2004), Vehicle Performance, Railway Civil Engineering Course notes

2004. Brisbane, Australia.

Standards Australia (1993), AS1170.4 Structural Design Action - Earthquake

Loading Code, Standards Australia. Sydney, Australia.

Standards Australia (1998), AS4100 Steel Structures, Standards Australia. Sydney,

Australia.

Standards Australia (2001), AS3600 Concrete Structures, Standards Australia.

Sydney, Australia.

Standards Australia (2002), AS1170.2 Structural Design Action - Wind Actions,

Standards Australia. Sydney, Australia.

Standard Australia (2002), AS/NZS1170 .0 Structural Design Actions, Part 0:

General Principles, Standard Australia. Sydney, Australia.

Standards Australia (2003), AS1085.14 Railway Track Material, Part 14: Prestressed

Concrete Sleepers, Standards Australia. Sydney, Australia.

Steffens, D. (2004), Identification and Development of a Model of Railway Track

Dynamic Behaviour, Masters of Engineering Thesis, Queensland University of

Technology, Brisbane, Australia.

Steffens, D. (2004), Instructions for Benchmark II, Queensland University of

Technology. Brisbane, Australia.

Steffens, D. and M. H. Murray, (2005), Establishing meaningful results from models

of railway track dynamic behaviour, International Heavy Haul Conference 2005,

June 14 - 16, Rio De Janeiro, Brazil.

167

Sun, Y. Q. (2003), A Wagon-Track System Dynamics Model for the Simulation of

Heavy Haul Railway Transportation, PhD Thesis, Central Queensland University,

Rockhampton, Australia.

RIC (2005), ARTC Standards - Prestressed Concrete Sleepers - Design, Railway

Infrastructure Corporation. Sydney, Australia.

Teknis (2003), Teknis Wheel Condition Monitor - Data Guide, Teknis. Adelaide,

Australia.

Tunna, J. M., (1988), Wheel/Rail Forces due to Wheel Irregularities, Proc. 9th Int.

Wheelset Congress, September 1988, Montreal, Canada.

Wakui, H. and H. Okuda, (1999), A Study on Limit State Design Method for

Prestressed Concrete Sleepers, Concrete Library of JSCE, Vol 33 pp. pp. 1-27.

Wright, D., (2000), Structural, Strength and Safety, [online]

<www.mech.uwa.edu.au/DANotes/SSS/safety/safety> Accessed [18/4/05].

Zhang, Y.-J. (2000), An Intergrated Rail Track Degredation Model, PhD Thesis,

Queensland University of Technology, Brisbane, QLD.

Appendix A Vehicle & Track Parameters included in Dynamic Impact Factor Formulae (Tew et al, 1991)

halla

This appendix is not available online. Please consult the hardcopy thesis available from the QUT Library

Appendix B Benchmark II instructions for Models of Railway Track Dynamic Behaviour (Steffens, 2004)

halla


Appendix C Calibration factors for Lara field instrumentation

halla


Appendix D Benchmark II results

Appendix D1 Simulation 1 RQTY Wagon 52t at 101.7km/hr Ideal Longitudinal Rail Profile

1D - Acceleration at End of Sleeper C

-30

-20

-10

0

10

20

30

0.400 0.420 0.440 0.460 0.480 0.500 0.520 0.540

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C

Figure D1.1; Sim 1D – Vertical Acceleration at End of Sleeper C for ‘Ideal’ Longitudinal Rail Profile

1E - Acceleration at Mid Span of Sleeper C

-30

-20

-10

0

10

20

30

0.400 0.420 0.440 0.460 0.480 0.500 0.520 0.540

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C

Figure D1.2; Sim 1E – Vertical Acceleration at Mid Span of Sleeper C for ‘Ideal’ Longitudinal Rail Profile

Appendix D2 Simulation 2 RQTY Wagon 78t at 110.8km/hr Ideal Longitudinal Rail Profile


60

70

80

90

100

110

120

130

0.300 0.350 0.400 0.450 0.500 0.550 0.600

Time (s)

Con

tact

For

ce (k

N)


Sleeper C

Figure D2.1; Sim 2A – Wheel/Rail Contact Force for Leading Wheel for ‘Ideal’ Longitudinal Rail


-80

-60

-40

-20

0

20

40

60

0.300 0.350 0.400 0.450 0.500 0.550 0.600

Time (s)

Shea

r For

ce (k

N) DARTS


Sleeper C

Figure D2.2; Sim 2B – Shear Force in Rail for ‘Ideal’ Longitudinal Rail


-50

-40

-30

-20

-10

0

10

20

30

40

50

0.350 0.370 0.390 0.410 0.430 0.450 0.470 0.490

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C

Figure D2.3; Sim 2C – Vertical Acceleration of the Rail at Midspan before Sleeper C for ‘Ideal’ Longitudinal Rail


-40

-30

-20

-10

0

10

20

30

40

0.300 0.350 0.400 0.450 0.500 0.550

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C



-40

-30

-20

-10

0

10

20

30

40

0.300 0.350 0.400 0.450 0.500 0.550

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C



-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

0.200 0.250 0.300 0.350 0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C

Figure D2.6; Sim 2F – Sleeper Bending Moment at Rail Seat for ‘Ideal’ Longitudinal Rail Profile


-5.5

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.150 0.250 0.350 0.450 0.550 0.650 0.750

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C

Figure D2.7; Sim 2G – Sleeper Bending Moment at Centre for ‘Ideal’ Longitudinal Rail Profile

Appendix D3 Simulation 3 RKWF Wagon 28t at 75.0km/hr Ideal Longitudinal Rail Profile


20

25

30

35

40

45

50

0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Con

tact

For

ce (k

N)


Sleeper C



-25

-20

-15

-10

-5

0

5

10

15

20

25

0.350 0.400 0.450 0.500 0.550 0.600 0.650 0.700 0.750 0.800

Time (s)

Shea

r For

ce (k

N) DARTS


Sleeper C



-50

-40

-30

-20

-10

0

10

20

30

40

50

0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C



-60

-50

-40

-30

-20

-10

0

10

20

30

0.224 0.324 0.424 0.524 0.624 0.724

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C



-10

-8

-6

-4

-2

0

2

4

6

8

10

0.25 0.35 0.45 0.55 0.65 0.75 0.85

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C



-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

0.200 0.300 0.400 0.500 0.600 0.700 0.800

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C



-6.5

-6.0

-5.5

-5.0

-4.5

-4.0

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

0.150 0.250 0.350 0.450 0.550 0.650 0.750

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C


Appendix D4 Simulation 4 RKWF Wagon 100t at 83.1km/hr Ideal Longitudinal Rail Profile


80

90

100

110

120

130

140

150

160

170

0.300 0.350 0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Con

tact

For

ce (k

N)


Sleeper C



-80

-60

-40

-20

0

20

40

60

80

0.350 0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Shea

r For

ce (k

N) DARTS


Sleeper C



-50

-40

-30

-20

-10

0

10

20

30

40

50

0.400 0.420 0.440 0.460 0.480 0.500 0.520 0.540 0.560 0.580 0.600

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C



-60

-50

-40

-30

-20

-10

0

10

20

30

40

0.200 0.300 0.400 0.500 0.600 0.700 0.800

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C



-40

-30

-20

-10

0

10

20

30

40

0.250 0.300 0.350 0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Acc

eler

atio

n (m

/s2 )


Sleeper C



-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

0.250 0.300 0.350 0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C



-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

0.150 0.250 0.350 0.450 0.550 0.650 0.750

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C


Appendix D5 Simulation 5 RQTY Wagon 52t at 101.7km/hr Actual Longitudinal Rail Profile


-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

0.400 0.410 0.420 0.430 0.440 0.450 0.460 0.470 0.480 0.490 0.500 0.510 0.520 0.530 0.540 0.550

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C

Figure D5.1; Sim 5D – Vertical Acceleration at End of Sleeper C for Actual Longitudinal Rail Profile


-100

-80

-60

-40

-20

0

20

40

60

80

100

0.400 0.420 0.440 0.460 0.480 0.500 0.520 0.540

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C

Figure D5.2; Sim 5E – Vertical Acceleration at Mid Span of Sleeper C for Actual Longitudinal Rail Profile

Appendix D6 Simulation 6 RQTY Wagon 78t at 110.8km/hr Actual Longitudinal Rail Profile


-40

-30

-20

-10

0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

0.259 0.309 0.359 0.409 0.459 0.509 0.559

Time (s)

Con

tact

For

ce (k

N)

-5

0

5

10

15

20D

epth (mm

)

DARTSDIFFDTRACKNUCARSSUBTTIVIAProfile 1, To Melbourne

Sleeper C

Figure D6.1; Sim 6A – Wheel/Rail Contact Force for Leading Wheel for Actual Longitudinal

Rail


-80

-60

-40

-20

0

20

40

60

80

100

120

0.350 0.400 0.450 0.500 0.550

Time (s)

Shea

r For

ce (k

N) DARTS


Sleeper C

Figure D6.2; Sim 6B – Shear Force in Rail for Actual Longitudinal Rail


-1500

-1000

-500

0

500

1000

1500

0.300 0.350 0.400 0.450 0.500 0.550

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C

Figure D6.3; Sim 6C – Vertical Acceleration of the Rail at Midspan before Sleeper C for Actual Longitudinal Rail


-400

-300

-200

-100

0

100

200

300

400

0.350 0.400 0.450 0.500 0.550

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C



-150

-100

-50

0

50

100

150

0.300 0.350 0.400 0.450 0.500 0.550

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C



-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

0.200 0.250 0.300 0.350 0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C

Figure D6.6; Sim 6F – Sleeper Bending Moment at Rail Seat for Actual Longitudinal Rail Profile


-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

0.150 0.250 0.350 0.450 0.550 0.650 0.750

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C

Figure D6.7; Sim 6G – Sleeper Bending Moment at Centre for Actual Longitudinal Rail Profile

Appendix D7 Simulation 7 RKWF Wagon 28t at 75.0km/hr Actual Longitudinal Rail Profile


-40

-30

-20

-10

0

10

20

30

40

50

60

70

80

0.300 0.350 0.400 0.450 0.500 0.550 0.600 0.650 0.700 0.750 0.800

Time (s)

Con

tact

For

ce (k

N)

-5

0

5

10

15

20

Depth (m

m)

DARTSDIFFDTRACKNUCARSSUBTTIVIAProfile 2, To Geelong

Sleeper C


Rail


-30

-20

-10

0

10

20

30

40

50

0.450 0.500 0.550 0.600 0.650 0.700 0.750

Time (s)

Shea

r For

ce (k

N) DARTS


Sleeper C



-500

-400

-300

-200

-100

0

100

200

300

400

500

0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C



-80

-60

-40

-20

0

20

40

60

80

0.414 0.464 0.514 0.564 0.614 0.664 0.714 0.764

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C



-80

-60

-40

-20

0

20

40

60

80

0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C



-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

0.200 0.300 0.400 0.500 0.600 0.700 0.800

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C



-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

0.250 0.350 0.450 0.550 0.650 0.750 0.850

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C


Appendix D8 Simulation 8 RKWF Wagon 100t at 83.1km/hr Actual Longitudinal Rail Profile


0

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

180

0.300 0.350 0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Con

tact

For

ce (k

N)

-5

0

5

10

15

20D

epth (mm

)

DARTSDIFFDTRACKNUCARSSUBTTIVIAProfile 1, To Melbourne

Sleeper C


Rail


-100

-50

0

50

100

150

0.400 0.450 0.500 0.550 0.600 0.650

Time (s)

Shea

r For

ce (k

N) DARTS


Sleeper C



-2000

-1500

-1000

-500

0

500

1000

1500

2000

0.450 0.470 0.490 0.510 0.530 0.550

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C



-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

0.200 0.300 0.400 0.500 0.600 0.700 0.800

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C



-80

-60

-40

-20

0

20

40

60

0.350 0.400 0.450 0.500 0.550 0.600 0.650

Time (s)

Acc

eler

atio

n (m

/s2 ) DARTS


Sleeper C



-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0.200 0.250 0.300 0.350 0.400 0.450 0.500 0.550 0.600 0.650 0.700

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C



-8.0

-7.0

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

4.0

5.0

0.150 0.250 0.350 0.450 0.550 0.650 0.750

Time (s)

Ben

ding

Mom

ent (

kNm

)


Sleeper C


Appendix E Summary of Benchmark Output Parameters Results for all Simulations

Table E1 Summary of Max/Min Output Results from Simulation 1

OU

TPU

T

PAR

AM

ET

ER

DA

RT

S

DIF

F

DT

RA

CK

NU

CA

RST

M

SUB

TT

I

VIA

Max (kN) 67.49 65.01 76.54 63.99 84.02 65.87

Avg (kN) 65.03 63.76 65.65 63.70 62.69 63.75 A

Min (kN) 62.46 61.89 58.87 63.14 41.37 61.63

31.18 30.43 14.53 34.15 22.19 28.69 B (kN)

-31.29 -33.04 -14.88 -34.17 -30.45 -28.50

1.58 18.33 5.12 6.04 496.10 3.97 C (m/s2)

-1.94 -12.70 -6.94 -3.37 -412.04 -1.79

9.06 9.06 1.35 9.89 24.70 1.98 D (m/s2)

-12.32 -12.32 -4.35 -3.19 -51.77 -1.19

1.25 11.53 0.97 12.59 24.70 1.59 E (m/s2)

-2.94 -10.56 -2.52 -4.46 -51.77 -1.19

2.21 3.32 3.96 4.70 4.47 2.12 F (kNm)

-0.09 -0.14 -1.70 -0.03 -0.14 -0.09

0.12 0.12 1.87 1.02 0.01 0.14 G (kNm)

-3.12 -3.02 -2.86 -1.27 -0.34 -3.09


OU

TPU

T

PAR

AM

ET

ER

DA

RT

S

DIF

F

DT

RA

CK

NU

CA

RST

M

SUB

TT

I

VIA

Max (kN) 101.77 97.44 100.09 95.89 126.94 99.72

Avg (kN) 97.55 95.64 95.66 95.54 94.01 95.62 A

Min (kN) 93.54 92.50 87.23 94.69 61.05 91.56

46.56 45.76 21.82 51.15 34.46 42.64 B (kN)

-46.49 -49.78 -22.43 -58.37 -52.70 -43.11

2.63 24.59 8.40 12.47 1063.15 9.47 C (m/s2)

-3.11 -20.35 -9.47 -6.08 -938.72 -3.31

2.21 15.20 2.29 16.79 28.53 4.02 D (m/s2)

-5.35 -20.04 -8.24 -5.89 -51.70 -2.37

2.08 18.55 1.69 21.32 28.53 3.31 E (m/s2)

-5.01 -15.21 -4.78 -7.78 -51.70 -2.13

3.31 4.96 5.97 7.05 6.79 3.23 F (kNm)

-0.13 -0.21 -2.56 -0.05 -0.28 -0.15

0.19 0.18 2.81 1.76 0.02 0.21 G (kNm)

-4.68 -4.53 -4.33 -1.90 -0.52 -4.71


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Max (kN) 36.01 35.53 35.80 34.46 44.74 34.78

Avg (kN) 35.01 34.33 34.34 34.30 33.76 34.32 A

Min (kN) 33.96 33.36 32.28 34.17 22.73 33.87

16.82 15.84 7.90 18.42 12.77 15.58 B (kN)

-16.99 -17.61 -7.95 -18.50 -19.42 -15.27

0.45 4.94 2.77 2.42 412.19 1.09 C (m/s2)

-0.92 -3.90 -3.10 -1.04 -421.01 -0.43

2.02 2.26 0.41 3.71 24.68 0.43 D (m/s2)

-4.30 -3.38 -1.74 -1.23 -51.84 -0.33

1.91 2.76 0.34 4.71 24.68 0.43 E (m/s2)

-4.03 -2.97 -1.01 -1.20 -51.84 -0.33

4.24 1.81 2.16 2.54 2.45 1.13 F (kNm)

-0.16 -0.07 -0.90 -0.02 -0.14 -0.05

0.23 0.06 1.00 0.38 0.01 0.07 G (kNm)

-6.00 -1.65 -1.50 -0.69 -0.19 -1.65


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Max (kN) 128.75 125.56 131.21 123.03 159.17 124.70

Avg (kN) 125.07 122.60 122.95 122.50 120.57 122.59 A

Min (kN) 121.14 119.58 112.15 121.90 81.56 120.48

59.68 57.70 28.22 65.72 40.30 55.64 B (kN)

-60.29 -63.20 -28.49 -66.19 -69.63 -54.54

2.01 19.21 9.53 8.84 1684.29 5.41 C (m/s2)

-3.68 -16.18 -11.18 -4.04 -1515.99 -2.70

2.02 10.83 1.67 13.47 32.23 1.62 D (m/s2)

-4.30 -15.40 -7.26 -4.50 -51.63 -1.22

1.91 13.19 1.43 17.74 32.23 1.35 E (m/s2)

-4.03 -13.66 -4.30 -5.60 -51.63 -0.95

4.24 6.43 7.72 9.08 8.60 4.04 F (kNm)

-0.16 -0.25 -3.25 -0.06 -0.37 -0.17

0.23 0.23 3.60 1.38 0.03 0.25 G (kNm)

-6.00 -5.86 -5.40 -2.46 -0.66 -5.88


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Max (kN) 86.41 89.50 95.04 105.10 152.07 91.92 -

Avg (kN) 65.02 63.76 63.75 63.82 62.56 63.70 - A

Min (kN) 45.57 44.80 33.82 25.93 0.00 31.24 -

30.87 29.95 16.65 50.68 18.56 28.49 77.02* B (kN)

-24.65 -38.73 -17.65 -47.88 -41.76 -36.21 -10.55*

13.55 268.78 172.44 246.98 412.88 377.50 379.42 C (m/s2)

-17.96 -179.55 -196.13 -292.23 -285.54 -574.29 -391.13

6.35 57.14 87.15 25.73 36.70 114.66 115.95 D (m/s2)

-6.65 -70.18 -76.65 -35.24 -51.77 -139.85 -159.08

6.00 59.04 86.82 54.98 36.70 72.60 78.23 E (m/s2)

-6.23 -66.31 -82.98 -73.19 -51.77 -87.09 -89.74

2.42 4.42 5.77 6.54 5.38 3.20 1.21 F (kNm)

-0.10 -0.24 -1.74 -0.04 -0.22 -0.25 -0.14

0.14 0.18 2.11 2.26 0.02 0.26 0.05 G (kNm)

-3.42 -4.07 -3.41 -3.07 -0.42 -4.51 -2.92

* the Lara field data collected is not a representation of the shear force in the rail. It is a measure of the wheel/rail force and its (maximum-minimum) value should be equal to the peak change in shear force.


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Max (kN) 124.41 122.49 141.18 135.09 145.41 130.31 -

Avg (kN) 97.58 95.60 95.64 95.55 94.00 95.61 - A

Min (kN) 73.56 65.02 64.75 38.72 38.30 50.10 -

49.01 46.47 28.30 53.67 35.24 47.12 103.40* B (kN)

-44.84 -52.22 -20.04 -58.42 -56.63 -48.12 -10.55*

10.28 196.87 0.10 356.00 1053.65 330.18 428.24 C (m/s2)

-16.00 -211.70 -239.90 -321.65 -1083.63 -299.41 -534.85

12.02 97.29 67.95 27.87 51.74 95.15 54.47 D (m/s2)

-18.04 -96.75 -74.31 -35.04 -51.70 -131.36 -63.56

11.22 101.30 84.80 84.53 51.74 63.67 58.12 E (m/s2)

-16.95 -92.14 -111.27 -97.80 -51.70 -81.89 -69.62

3.99 5.43 6.73 9.24 7.44 3.92 1.77 F (kNm)

-0.13 -0.27 -3.53 -0.06 -0.31 -0.27 -0.30

0.19 0.23 2.85 2.16 0.02 0.32 0.21 G (kNm)

-5.68 -4.95 -4.27 -3.85 -0.57 -5.61 -3.52



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Max (kN) 55.83 60.55 51.24 64.42 117.24 48.37 -

Avg (kN) 35.01 34.33 34.34 34.33 33.65 34.32 - A

Min (kN) 14.02 9.48 17.16 5.05 0.00 16.12 -

20.49 12.02 8.64 22.08 10.10 16.05 37.98 B (kN)

-11.67 -23.93 -10.20 -25.08 -23.13 -20.61 -9.50

7.63 104.36 89.82 128.76 389.38 210.61 138.64 C (m/s2)

-8.17 -79.94 -110.82 -171.44 -368.68 -313.26 -112.08

3.52 21.73 30.62 16.94 24.68 57.18 44.41 D (m/s2)

-3.94 -27.82 -43.76 -29.36 -51.84 -73.24 -27.53

3.32 19.86 41.96 37.00 24.68 32.34 55.55 E (m/s2)

-3.71 -30.86 -32.67 -53.83 -51.84 -44.60 -63.41

1.37 3.20 3.35 3.98 2.78 1.68 0.23 F (kNm)

-0.05 -0.14 -0.96 -0.09 -0.14 -0.12 -0.55

0.08 0.11 1.21 1.47 0.01 0.13 0.14 G (kNm)

-1.94 -2.91 -2.05 -2.89 -0.21 -2.34 -2.01



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Max (kN) 145.49 146.51 149.67 154.71 169.92 144.25 -

Avg (kN) 125.11 122.60 122.95 122.56 120.48 122.62 - A

Min (kN) 104.55 99.70 103.13 96.32 68.07 97.74 -

63.44 56.57 31.96 75.61 41.70 57.96 127.67 B (kN)

-59.40 -64.28 -28.06 -67.88 -73.36 -54.10 -5.28

5.50 128.01 114.60 209.86 1707.69 215.12 159.15 C (m/s2)

-8.81 -130.83 -132.75 -177.25 -1663.11 -202.54 -136.88

7.72 28.56 36.58 21.96 32.24 71.80 16.64 D (m/s2)

-11.89 -40.23 -29.43 -28.62 -51.63 -99.38 -21.78

7.23 29.21 44.58 50.26 32.24 53.27 28.43 E (m/s2)

-11.18 -38.28 -47.01 -57.00 -51.63 -61.11 -34.77

4.76 7.09 8.66 10.36 8.89 4.53 1.27 F (kNm)

-0.16 -0.30 -3.78 -0.06 -0.39 -0.27 -0.17

0.23 0.26 3.64 1.46 0.03 0.32 0.31 G (kNm)

-6.75 -6.46 -5.28 -3.97 -0.69 -6.48 -3.99


Appendix F Drawing of typical concrete sleeper found at Braeside, Drawing Number No 201-05 and calculation of ultimate limit strength

halla


Documents

Development of a Limit State Design Methodology for Railway … · 2010. 6. 9. · revision of the ‘Permanent way materials: prestressed concrete sleepers’ code (AS1085.14, 2003)